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16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics.mp4 157.5 MB11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4 145.9 MB12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4 144.8 MB05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4 112.6 MB40 - Part 6 Mathematics/011 Why is Linear Algebra Useful.mp4 92.7 MB35 - Advanced Statistical Methods - Practical Example Linear Regression/001 Practical Example Linear Regression (Part 1).mp4 88.8 MB20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp4 87.6 MB64 - Appendix - Working with Text Files in Python/018 Importing Data from .json Files.mp4 85.9 MB05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp4 84.5 MB05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp4 79.8 MB64 - Appendix - Working with Text Files in Python/013 Importing .csv Files - Part III.mp4 78.7 MB51 - Deep Learning - Business Case Example/004 Business Case Preprocessing the Data.mp4 77.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp4 73.2 MB19 - Statistics - Practical Example Inferential Statistics/001 Practical Example Inferential Statistics.mp4 72.3 MB56 - Software Integration/003 Taking a Closer Look at APIs.mp4 70.3 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Business Case Preprocessing.mp4 66.8 MB05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp4 65.1 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset.mp4 63.2 MB56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints.mp4 63.2 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp4 62.1 MB08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 61.7 MB02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many.mp4 60.1 MB13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp4 59.6 MB64 - Appendix - Working with Text Files in Python/015 Importing Data with .loadtxt() and .genfromtxt().mp4 59.1 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider.mp4 59.0 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp4 56.7 MB05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp4 55.4 MB02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction.mp4 55.2 MB18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp4 54.7 MB01 - Part 1 Introduction/002 What Does the Course Cover.mp4 53.9 MB51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors.mp4 53.8 MB35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5).mp4 52.9 MB64 - Appendix - Working with Text Files in Python/011 Importing .csv Files - Part I.mp4 52.3 MB05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp4 51.8 MB04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 49.0 MB21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing.mp4 48.0 MB09 - Part 2 Probability/002 Computing Expected Values.mp4 47.9 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 MNIST Results and Testing.mp4 47.7 MB02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp4 47.5 MB60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp4 47.3 MB18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp4 47.2 MB01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp4 46.1 MB64 - Appendix - Working with Text Files in Python/016 Importing Data - Partial Cleaning While Importing Data.mp4 46.0 MB51 - Deep Learning - Business Case Example/009 Business Case Setting an Early Stopping Mechanism.mp4 45.9 MB62 - Appendix - Additional Python Tools/005 List Comprehensions.mp4 45.3 MB15 - Statistics - Descriptive Statistics/001 Types of Data.mp4 45.3 MB64 - Appendix - Working with Text Files in Python/021 Importing Data in Python - an Important Exercise.mp4 45.1 MB64 - Appendix - Working with Text Files in Python/019 An Introduction to Working with Excel Files in Python.mp4 45.1 MB56 - Software Integration/005 Software Integration - Explained.mp4 45.0 MB10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp4 44.9 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline.mp4 44.6 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp4 43.9 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp4 43.3 MB13 - Probability - Probability in Other Fields/001 Probability in Finance.mp4 42.3 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp4 42.2 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp4 42.1 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp4 41.9 MB35 - Advanced Statistical Methods - Practical Example Linear Regression/006 Practical Example Linear Regression (Part 4).mp4 41.3 MB20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp4 40.6 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp4 40.5 MB12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution.mp4 40.2 MB38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful.mp4 39.3 MB09 - Part 2 Probability/003 Frequency.mp4 39.2 MB63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp4 39.1 MB02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp4 38.7 MB20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp4 38.7 MB15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp4 38.4 MB12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp4 37.9 MB37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp4 37.6 MB12 - Probability - Distributions/002 Types of Probability Distributions.mp4 37.3 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp4 37.3 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp4 37.0 MB14 - Part 3 Statistics/001 Population and Sample.mp4 36.8 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline.mp4 36.3 MB40 - Part 6 Mathematics/010 Dot Product of Matrices.mp4 36.0 MB38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp4 35.9 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp4 35.9 MB38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp4 35.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the Date Column.mp4 35.6 MB20 - Statistics - Hypothesis Testing/007 p-value.mp4 35.4 MB62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp4 34.6 MB22 - Part 4 Introduction to Python/004 Installing Python and Jupyter.mp4 34.4 MB20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp4 34.4 MB50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch.mp4 34.3 MB28 - Python - Sequences/005 Dictionaries.mp4 34.0 MB63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp4 33.8 MB15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp4 33.7 MB35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2).mp4 33.4 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning.mp4 33.4 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp4 33.2 MB13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp4 33.1 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp4 33.1 MB50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning.mp4 32.5 MB12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution.mp4 32.1 MB28 - Python - Sequences/002 Using Methods.mp4 31.8 MB62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp4 31.8 MB63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp4 31.2 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp4 31.1 MB09 - Part 2 Probability/001 The Basic Probability Formula.mp4 30.8 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 30.3 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp4 30.1 MB18 - Statistics - Inferential Statistics Confidence Intervals/001 What are Confidence Intervals.mp4 30.0 MB64 - Appendix - Working with Text Files in Python/009 Importing Text Files - open().mp4 29.6 MB38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp4 29.5 MB51 - Deep Learning - Business Case Example/008 Business Case Learning and Interpreting the Result.mp4 29.1 MB05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp4 29.0 MB17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp4 29.0 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp4 29.0 MB17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp4 28.8 MB05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp4 28.7 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity.mp4 28.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp4 28.7 MB44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0.mp4 28.6 MB51 - Deep Learning - Business Case Example/003 Business Case Balancing the Dataset.mp4 28.6 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset.mp4 28.6 MB44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2.mp4 28.2 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization.mp4 28.2 MB38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp4 28.2 MB64 - Appendix - Working with Text Files in Python/010 Importing Text Files - with open().mp4 27.5 MB44 - Deep Learning - TensorFlow 2.0 Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp4 27.2 MB09 - Part 2 Probability/004 Events and Their Complements.mp4 27.1 MB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp4 27.0 MB62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp4 26.9 MB36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp4 26.0 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp4 26.0 MB05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp4 25.9 MB15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp4 25.7 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp4 25.6 MB20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp4 25.6 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp4 25.6 MB63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp4 25.5 MB57 - Case Study - What's Next in the Course/003 Introducing the Data Set.mp4 25.4 MB11 - Probability - Bayesian Inference/004 Union of Sets.mp4 25.4 MB49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp4 25.1 MB12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution.mp4 25.1 MB36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp4 24.9 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net.mp4 24.8 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp4 24.8 MB10 - Probability - Combinatorics/006 Solving Combinations.mp4 24.8 MB29 - Python - Iterations/001 For Loops.mp4 24.7 MB42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent.mp4 24.7 MB15 - Statistics - Descriptive Statistics/015 Variance.mp4 24.7 MB64 - Appendix - Working with Text Files in Python/026 Saving Your Data with NumPy - Part II - .npz.mp4 24.4 MB17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp4 24.3 MB18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error.mp4 24.2 MB28 - Python - Sequences/001 Lists.mp4 24.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 24.0 MB50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model.mp4 23.7 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp4 23.7 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS.mp4 23.6 MB64 - Appendix - Working with Text Files in Python/022 Importing Data with the .squeeze() Method.mp4 23.5 MB63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp4 23.3 MB40 - Part 6 Mathematics/006 Addition and Subtraction of Matrices.mp4 23.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model.mp4 23.2 MB29 - Python - Iterations/004 Conditional Statements and Loops.mp4 23.0 MB36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp4 23.0 MB56 - Software Integration/004 Communication between Software Products through Text Files.mp4 22.9 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp4 22.8 MB36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp4 22.6 MB11 - Probability - Bayesian Inference/011 Bayes' Law.mp4 22.4 MB63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp4 22.2 MB64 - Appendix - Working with Text Files in Python/024 Saving Your Data with pandas.mp4 22.1 MB63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp4 22.1 MB12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution.mp4 22.0 MB64 - Appendix - Working with Text Files in Python/027 Saving Your Data with NumPy - Part III - .csv.mp4 21.8 MB63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp4 21.7 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework.mp4 21.6 MB40 - Part 6 Mathematics/008 Transpose of a Matrix.mp4 21.5 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp4 21.5 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp4 21.4 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp4 21.3 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/007 Backpropagation.mp4 21.3 MB36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp4 21.3 MB11 - Probability - Bayesian Inference/010 The Multiplication Law.mp4 21.2 MB29 - Python - Iterations/002 While Loops and Incrementing.mp4 21.2 MB15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp4 21.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several Straightforward Columns for this Exercise.mp4 21.1 MB12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution.mp4 21.0 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp4 20.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp4 20.7 MB15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp4 20.7 MB20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp4 20.7 MB57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 20.6 MB60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp4 20.6 MB64 - Appendix - Working with Text Files in Python/023 Importing Files in Jupyter.mp4 20.5 MB63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp4 20.5 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp4 20.5 MB56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp4 20.5 MB12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp4 20.4 MB15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp4 20.3 MB11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp4 20.2 MB28 - Python - Sequences/003 List Slicing.mp4 20.1 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output.mp4 20.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column.mp4 20.0 MB25 - Python - Other Python Operators/002 Logical and Identity Operators.mp4 19.9 MB40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.9 MB22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp4 19.9 MB18 - Statistics - Inferential Statistics Confidence Intervals/004 Confidence Interval Clarifications.mp4 19.9 MB64 - Appendix - Working with Text Files in Python/025 Saving Your Data with NumPy - Part I - .npy.mp4 19.8 MB36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp4 19.7 MB20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp4 19.5 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 Business Case Interpretation.mp4 19.5 MB15 - Statistics - Descriptive Statistics/019 Covariance.mp4 19.3 MB05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp4 19.3 MB39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp4 19.2 MB10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp4 19.1 MB28 - Python - Sequences/004 Tuples.mp4 19.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp4 18.9 MB63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp4 18.7 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 18.6 MB15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp4 18.5 MB11 - Probability - Bayesian Inference/001 Sets and Events.mp4 18.5 MB10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp4 18.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp4 18.2 MB17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp4 18.0 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets.mp4 17.7 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/004 TensorFlow Intro.mp4 17.7 MB42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent.mp4 17.7 MB64 - Appendix - Working with Text Files in Python/005 Importing Data in Python - Principles.mp4 17.5 MB44 - Deep Learning - TensorFlow 2.0 Introduction/008 Customizing a TensorFlow 2 Model.mp4 17.5 MB10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp4 17.5 MB42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp4 17.5 MB35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3).mp4 17.5 MB11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp4 17.4 MB29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 17.2 MB10 - Probability - Combinatorics/009 Combinatorics in Real-Life The Lottery.mp4 17.2 MB12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution.mp4 17.0 MB52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp4 16.9 MB12 - Probability - Distributions/013 Continuous Distributions The Exponential Distribution.mp4 16.8 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm.mp4 16.5 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent.mp4 16.5 MB23 - Python - Variables and Data Types/003 Python Strings.mp4 16.4 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp4 16.4 MB
65 - Bonus Lecture/001 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 16.3 MB40 - Part 6 Mathematics/005 What is a Tensor.mp4 16.3 MB20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp4 16.2 MB44 - Deep Learning - TensorFlow 2.0 Introduction/003 TensorFlow 1 vs TensorFlow 2.mp4 16.1 MB44 - Deep Learning - TensorFlow 2.0 Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp4 16.0 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp4 16.0 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp4 15.9 MB12 - Probability - Distributions/005 Discrete Distributions The Bernoulli Distribution.mp4 15.9 MB10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp4 15.8 MB11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp4 15.6 MB22 - Part 4 Introduction to Python/001 Introduction to Programming.mp4 15.5 MB18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp4 15.3 MB36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp4 15.3 MB37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp4 15.2 MB64 - Appendix - Working with Text Files in Python/020 Working with Excel (.xlsx) Data.mp4 15.1 MB26 - Python - Conditional Statements/003 The ELIF Statement.mp4 14.9 MB46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp4 14.7 MB10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp4 14.6 MB51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data.mp4 14.5 MB07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp4 14.5 MB10 - Probability - Combinatorics/007 Symmetry of Combinations.mp4 14.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp4 14.4 MB40 - Part 6 Mathematics/003 Linear Algebra and Geometry.mp4 14.4 MB23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp4 14.4 MB18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp4 14.4 MB18 - Statistics - Inferential Statistics Confidence Intervals/005 Student's T Distribution.mp4 14.3 MB17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp4 14.2 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp4 14.1 MB52 - Deep Learning - Conclusion/004 An overview of CNNs.mp4 14.0 MB15 - Statistics - Descriptive Statistics/013 Skewness.mp4 14.0 MB64 - Appendix - Working with Text Files in Python/006 Plain Text Files, Flat Files and More.mp4 13.8 MB05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp4 13.7 MB42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp4 13.7 MB47 - Deep Learning - Initialization/001 What is Initialization.mp4 13.5 MB40 - Part 6 Mathematics/009 Dot Product.mp4 13.5 MB62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp4 13.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST Importing the Relevant Packages and Loading the Data.mp4 12.8 MB62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.7 MB49 - Deep Learning - Preprocessing/003 Standardization.mp4 12.7 MB22 - Part 4 Introduction to Python/002 Why Python.mp4 12.6 MB64 - Appendix - Working with Text Files in Python/001 An Introduction to Working with Files in Python.mp4 12.6 MB18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.6 MB29 - Python - Iterations/003 Lists with the range() Function.mp4 12.5 MB40 - Part 6 Mathematics/001 What is a Matrix.mp4 12.5 MB41 - Part 7 Deep Learning/001 What to Expect from this Part.mp4 12.3 MB64 - Appendix - Working with Text Files in Python/014 Importing Data with index_col.mp4 12.2 MB11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp4 12.1 MB36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp4 11.9 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages.mp4 11.8 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp4 11.7 MB02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp4 11.7 MB38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp4 11.7 MB11 - Probability - Bayesian Inference/009 The Additive Rule.mp4 11.6 MB64 - Appendix - Working with Text Files in Python/003 Structured, Semi-Structured and Unstructured Data.mp4 11.6 MB11 - Probability - Bayesian Inference/003 Intersection of Sets.mp4 11.5 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp4 11.5 MB64 - Appendix - Working with Text Files in Python/012 Importing .csv Files - Part II.mp4 11.5 MB38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp4 11.5 MB38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp4 11.3 MB64 - Appendix - Working with Text Files in Python/004 Text Files and Data Connectivity.mp4 11.3 MB46 - Deep Learning - Overfitting/001 What is Overfitting.mp4 11.3 MB10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp4 11.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer.mp4 11.1 MB63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp4 11.1 MB11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp4 11.1 MB42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp4 11.0 MB38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp4 10.9 MB12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution.mp4 10.8 MB46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp4 10.8 MB27 - Python - Python Functions/007 Built-in Functions in Python.mp4 10.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp4 10.5 MB52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp4 10.3 MB42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss.mp4 10.3 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp4 10.1 MB37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp4 10.1 MB15 - Statistics - Descriptive Statistics/007 The Histogram.mp4 10.0 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping.mp4 9.9 MB12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp4 9.9 MB64 - Appendix - Working with Text Files in Python/002 File vs File Object, Reading vs Parsing Data.mp4 9.9 MB46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp4 9.9 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp4 9.8 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity.mp4 9.7 MB12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution.mp4 9.7 MB49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp4 9.7 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow.mp4 9.6 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 What is a Deep Net.mp4 9.6 MB39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp4 9.4 MB63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp4 9.4 MB23 - Python - Variables and Data Types/001 Variables.mp4 9.4 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors.mp4 9.3 MB44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow.mp4 9.3 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions.mp4 9.3 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp4 9.2 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation.mp4 9.2 MB30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp4 9.1 MB
12 - Probability - Distributions/015 FIFA19-post.csv 9.1 MB12 - Probability - Distributions/015 FIFA19.csv 9.1 MB24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp4 9.1 MB17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp4 9.0 MB36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp4 9.0 MB30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp4 9.0 MB40 - Part 6 Mathematics/002 Scalars and Vectors.mp4 8.9 MB46 - Deep Learning - Overfitting/003 What is Validation.mp4 8.8 MB57 - Case Study - What's Next in the Course/002 The Business Task.mp4 8.8 MB27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp4 8.7 MB64 - Appendix - Working with Text Files in Python/008 Common Naming Conventions.mp4 8.6 MB51 - Deep Learning - Business Case Example/011 Business Case Testing the Model.mp4 8.6 MB22 - Part 4 Introduction to Python/003 Why Jupyter.mp4 8.5 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture.mp4 8.5 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp4 8.4 MB42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp4 8.4 MB50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 8.3 MB42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp4 8.3 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation.mp4 8.3 MB42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp4 8.2 MB42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp4 8.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 8.0 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity.mp4 8.0 MB36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp4 7.9 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp4 7.7 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp4 7.5 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp4 7.5 MB52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp4 7.3 MB
02 - The Field of Data Science - The Various Data Science Disciplines/004 365-DataScience.png 7.3 MB02 - The Field of Data Science - The Various Data Science Disciplines/005 365-DataScience.png 7.3 MB18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp4 7.2 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 6.8 MB27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp4 6.8 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp4 6.8 MB46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp4 6.5 MB42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function.mp4 6.5 MB26 - Python - Conditional Statements/001 The IF Statement.mp4 6.4 MB22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 6.4 MB26 - Python - Conditional Statements/002 The ELSE Statement.mp4 6.3 MB27 - Python - Python Functions/005 Conditional Statements and Functions.mp4 6.3 MB10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp4 6.2 MB36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp4 6.2 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp4 6.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables A Statistical Perspective.mp4 6.1 MB40 - Part 6 Mathematics/007 Errors when Adding Matrices.mp4 6.1 MB47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp4 6.0 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp4 6.0 MB42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss.mp4 5.7 MB47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.7 MB49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp4 5.7 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section.mp4 5.5 MB37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp4 5.5 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp4 5.5 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp4 5.4 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer.mp4 5.4 MB30 - Python - Advanced Python Tools/003 What is the Standard Library.mp4 5.3 MB53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1.mp4 5.2 MB64 - Appendix - Working with Text Files in Python/007 Text Files of Fixed Width.mp4 5.1 MB25 - Python - Other Python Operators/001 Comparison Operators.mp4 5.1 MB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset.mp4 5.0 MB52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp4 5.0 MB44 - Deep Learning - TensorFlow 2.0 Introduction/004 A Note on TensorFlow 2 Syntax.mp4 4.9 MB50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset.mp4 4.8 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model.mp4 4.5 MB29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp4 4.5 MB26 - Python - Conditional Statements/004 A Note on Boolean Values.mp4 4.4 MB55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution.mp4 4.4 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp4 4.0 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp4 3.8 MB31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp4 3.8 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1 Linearity.mp4 3.7 MB38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp4 3.7 MB49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp4 3.4 MB27 - Python - Python Functions/001 Defining a Function in Python.mp4 3.4 MB27 - Python - Python Functions/004 How to Use a Function within a Function.mp4 3.4 MB17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp4 3.2 MB51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution.mp4 3.2 MB27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp4 3.0 MB24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp4 2.9 MB24 - Python - Basic Python Syntax/002 The Double Equality Sign.mp4 2.8 MB24 - Python - Basic Python Syntax/004 Add Comments.mp4 2.5 MB24 - Python - Basic Python Syntax/006 Indexing Elements.mp4 2.5 MB22 - Part 4 Introduction to Python/001 Introduction-to-Python-Course-Notes.pdf 2.3 MB23 - Python - Variables and Data Types/001 Introduction-to-Python-Course-Notes.pdf 2.3 MB64 - Appendix - Working with Text Files in Python/029 Working with Text Files in Python - Conclusion.mp4 2.2 MB30 - Python - Advanced Python Tools/002 Modules and Packages.mp4 2.2 MB24 - Python - Basic Python Syntax/003 How to Reassign Values.mp4 2.0 MB19 - Statistics - Practical Example Inferential Statistics/002 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB19 - Statistics - Practical Example Inferential Statistics/001 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.8 MB19 - Statistics - Practical Example Inferential Statistics/002 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.8 MB24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp4 1.3 MB20 - Statistics - Hypothesis Testing/007 Online-p-value-calculator.pdf 1.2 MB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 Course-Notes-Section-6.pdf 958.9 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 Course-Notes-Section-6.pdf 958.9 kB11 - Probability - Bayesian Inference/012 CDS-2017-2018-Hamilton.pdf 865.6 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB51 - Deep Learning - Business Case Example/001 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/012 Audiobooks-data.csv 727.8 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 715.1 kB20 - Statistics - Hypothesis Testing/001 Course-notes-hypothesis-testing.pdf 672.2 kB20 - Statistics - Hypothesis Testing/003 Course-notes-hypothesis-testing.pdf 672.2 kB64 - Appendix - Working with Text Files in Python/001 Common-Naming-Conventions.pdf 659.2 kB64 - Appendix - Working with Text Files in Python/008 Common-Naming-Conventions.pdf 659.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Shortcuts-for-Jupyter.pdf 634.0 kB44 - Deep Learning - TensorFlow 2.0 Introduction/001 Shortcuts-for-Jupyter.pdf 634.0 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Shortcuts-for-Jupyter.pdf 634.0 kB42 - Deep Learning - Introduction to Neural Networks/001 Course-Notes-Section-2.pdf 592.0 kB42 - Deep Learning - Introduction to Neural Networks/002 Course-Notes-Section-2.pdf 592.0 kB14 - Part 3 Statistics/001 Course-notes-descriptive-statistics.pdf 493.8 kB15 - Statistics - Descriptive Statistics/001 Course-notes-descriptive-statistics.pdf 493.8 kB12 - Probability - Distributions/001 Course-Notes-Probability-Distributions.pdf 475.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/006 sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 417.4 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/006 sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 406.8 kB11 - Probability - Bayesian Inference/001 Course-Notes-Bayesian-Inference.pdf 395.3 kB17 - Statistics - Inferential Statistics Fundamentals/001 Course-notes-inferential-statistics.pdf 391.5 kB17 - Statistics - Inferential Statistics Fundamentals/002 Course-notes-inferential-statistics.pdf 391.5 kB09 - Part 2 Probability/001 Course-Notes-Basic-Probability.pdf 380.0 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/005 sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 379.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/004 sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 359.9 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/005 sklearn-Dummies-and-VIF-Exercise.ipynb 352.9 kB12 - Probability - Distributions/008 Solving-Integrals.pdf 352.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/004 sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 351.8 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 343.7 kB36 - Advanced Statistical Methods - Logistic Regression/001 Course-Notes-Logistic-Regression.pdf 343.2 kB36 - Advanced Statistical Methods - Logistic Regression/002 Course-Notes-Logistic-Regression.pdf 343.2 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB02 - The Field of Data Science - The Various Data Science Disciplines/003 365-DataScience-Diagram.pdf 330.8 kB02 - The Field of Data Science - The Various Data Science Disciplines/004 365-DataScience-Diagram.pdf 330.8 kB13 - Probability - Probability in Other Fields/003 Probability-Cheat-Sheet.pdf 328.0 kB31 - Part 5 Advanced Statistical Methods in Python/001 Course-notes-regression-analysis.pdf 319.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 Course-notes-regression-analysis.pdf 319.7 kB01 - Part 1 Introduction/003 FAQ-The-Data-Science-Course.pdf 313.4 kB15 - Statistics - Descriptive Statistics/004 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB15 - Statistics - Descriptive Statistics/008 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics-Solutions.pdf 251.6 kB10 - Probability - Combinatorics/001 Course-Notes-Combinatorics.pdf 231.5 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/001 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/002 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/005 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/006 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/008 1.04.Real-life-example.csv 225.1 kB64 - Appendix - Working with Text Files in Python/018 Lending-company.json 218.7 kB37 - Advanced Statistical Methods - Cluster Analysis/001 Course-Notes-Cluster-Analysis.pdf 213.7 kB37 - Advanced Statistical Methods - Cluster Analysis/002 Course-Notes-Cluster-Analysis.pdf 213.7 kB10 - Probability - Combinatorics/006 Combinations-With-Repetition.pdf 212.4 kB13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Solutions.pdf 188.9 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/009 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 175.5 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 170.9 kB63 - Appendix - pandas Fundamentals/001 Sales-products.csv 155.9 kB63 - Appendix - pandas Fundamentals/012 Sales-products.csv 155.9 kB16 - Statistics - Practical Example Descriptive Statistics/001 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 150.0 kB16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 149.9 kB12 - Probability - Distributions/007 Poisson-Expected-Value-and-Variance.pdf 149.5 kB12 - Probability - Distributions/009 Normal-Distribution-Exp-and-Var.pdf 147.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/001 data-preprocessing-homework.pdf 137.7 kB16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 123.2 kB63 - Appendix - pandas Fundamentals/001 pandas-Fundamentals-Solutions.ipynb 121.2 kB63 - Appendix - pandas Fundamentals/012 pandas-Fundamentals-Solutions.ipynb 121.2 kB64 - Appendix - Working with Text Files in Python/011 Lending-company-single-column-data.csv 117.2 kB63 - Appendix - pandas Fundamentals/001 Lending-company.csv 115.1 kB63 - Appendix - pandas Fundamentals/012 Lending-company.csv 115.1 kB64 - Appendix - Working with Text Files in Python/011 Lending-company.csv 115.1 kB36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Solution.ipynb 113.8 kB13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Homework.pdf 113.3 kB10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics.pdf 109.1 kB64 - Appendix - Working with Text Files in Python/020 Lending-company.xlsx 95.3 kB10 - Probability - Combinatorics/007 Symmetry-Explained.pdf 87.1 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 85.7 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-example-All-exercises.ipynb 85.6 kB44 - Deep Learning - TensorFlow 2.0 Introduction/008 TensorFlow-Minimal-example-complete-with-comments.ipynb 84.3 kB36 - Advanced Statistical Methods - Logistic Regression/013 Calculating-the-Accuracy-of-the-Model-Solution.ipynb 83.2 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 79.4 kB44 - Deep Learning - TensorFlow 2.0 Introduction/008 TensorFlow-Minimal-example-complete.ipynb 78.7 kB44 - Deep Learning - TensorFlow 2.0 Introduction/007 TensorFlow-Minimal-example-Part3.ipynb 78.4 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.c.Solution.ipynb 71.8 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-1-Solution.ipynb 70.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-5-Solution.ipynb 70.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.a.Solution.ipynb 69.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.b.Solution.ipynb 69.3 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-4-Solution.ipynb 68.1 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251170.mpd 66.9 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251152.mpd 65.3 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15185936.mpd 64.8 kB60 - Case Study - Loading the 'absenteeism_module'/001 Absenteeism-Exercise-Integration.ipynb 63.8 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-6-Solution.ipynb 63.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-6.ipynb 63.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-2-Solution.ipynb 62.9 kB06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.m4a.part.frag.urls 62.5 kB06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.mp4.part.frag.urls 62.5 kB03 - The Field of Data Science - Connecting the Data Science Disciplines/temp/index_13908866.mpd 60.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15256016.mpd 60.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/temp/index_13060600.mpd 60.3 kB64 - Appendix - Working with Text Files in Python/025 Lending-Company-Saving.csv 59.8 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15293084.mpd 58.1 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251158.mpd 57.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/temp/index_13060526.mpd 56.0 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251164.mpd 56.0 kB21 - Statistics - Practical Example Hypothesis Testing/001 4.10.Hypothesis-testing-section-practical-example.xlsx 53.1 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 51.2 kB21 - Statistics - Practical Example Hypothesis Testing/002 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 45.3 kB21 - Statistics - Practical Example Hypothesis Testing/002 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 44.7 kB42 - Deep Learning - Introduction to Neural Networks/011 GD-function-example.xlsx 43.4 kB06 - The Field of Data Science - Popular Data Science Tools/temp/index_13040208.mpd 42.1 kB15 - Statistics - Descriptive Statistics/004 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 42.1 kB15 - Statistics - Descriptive Statistics/010 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 41.4 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061100.mpd 40.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251168.mpd 40.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251154.mpd 39.0 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/temp/index_17572166.mpd 38.7 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061110.mpd 36.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.encrypted.mp4.part.frag.urls 36.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.encrypted.m4a.part.frag.urls 36.7 kB15 - Statistics - Descriptive Statistics/013 2.8.Skewness-lesson.xlsx 35.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/001 Absenteeism-data.csv 32.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/temp/index_17596538.mpd 32.0 kB63 - Appendix - pandas Fundamentals/001 pandas-Fundamentals-Exercises.ipynb 31.7 kB63 - Appendix - pandas Fundamentals/012 pandas-Fundamentals-Exercises.ipynb 31.7 kB15 - Statistics - Descriptive Statistics/003 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31.5 kB11 - Probability - Bayesian Inference/012 Bayesian-Homework-Solutions.pdf 31.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 30.5 kB64 - Appendix - Working with Text Files in Python/015 Lending-Company-Numeric-Data.csv 30.2 kB15 - Statistics - Descriptive Statistics/020 2.11.Covariance-exercise-solution.xlsx 30.2 kB15 - Statistics - Descriptive Statistics/022 2.12.Correlation-exercise-solution.xlsx 30.2 kB15 - Statistics - Descriptive Statistics/022 2.12.Correlation-exercise.xlsx 30.0 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Absenteeism-preprocessed.csv 29.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/001 df-preprocessed.csv 29.8 kB64 - Appendix - Working with Text Files in Python/015 Lending-Company-Numeric-Data-NAN.csv 29.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 sklearn-Simple-Linear-Regression-with-comments.ipynb 29.0 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.6 kB64 - Appendix - Working with Text Files in Python/001 Working-with-Text-Files-Lectures.ipynb 28.2 kB64 - Appendix - Working with Text Files in Python/029 Working-with-Text-Files-Lectures.ipynb 28.2 kB11 - Probability - Bayesian Inference/012 Bayesian-Homework.pdf 27.9 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.6 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 27.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/006 Simple-Linear-Regression-with-sklearn-Exercise-Solution.ipynb 27.2 kB15 - Statistics - Descriptive Statistics/009 2.6.Cross-table-and-scatter-plot.xlsx 26.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 sklearn-Simple-Linear-Regression.ipynb 26.7 kB18 - Statistics - Inferential Statistics Confidence Intervals/002 3.9.The-z-table.xlsx 26.2 kB18 - Statistics - Inferential Statistics Confidence Intervals/003 3.9.The-z-table.xlsx 26.2 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 26.2 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 26.1 kB12 - Probability - Distributions/015 A Practical Example of Probability Distributions_en.srt 26.1 kB62 - Appendix - Additional Python Tools/001 Additional-Python-Tools-Solutions.ipynb 26.1 kB62 - Appendix - Additional Python Tools/006 Additional-Python-Tools-Solutions.ipynb 26.1 kB16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics_en.srt 26.0 kB15 - Statistics - Descriptive Statistics/019 2.11.Covariance-lesson.xlsx 25.5 kB64 - Appendix - Working with Text Files in Python/017 Importing-Text-Data-DSc-Solution.ipynb 25.0 kB11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference_en.srt 24.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.encrypted.m4a.part.frag.urls 24.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.encrypted.mp4.part.frag.urls 24.8 kB17 - Statistics - Inferential Statistics Fundamentals/005 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.6 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 24.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.6 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 22.3 kB63 - Appendix - pandas Fundamentals/001 pandas-Fundamentals-Lectures.ipynb 21.8 kB63 - Appendix - pandas Fundamentals/012 pandas-Fundamentals-Lectures.ipynb 21.8 kB
01 - Part 1 Introduction/003 Download All Resources and Important FAQ.html 21.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 21.1 kB14 - Part 3 Statistics/001 Statistics-Glossary.xlsx 20.8 kB15 - Statistics - Descriptive Statistics/020 2.11.Covariance-exercise.xlsx 20.7 kB12 - Probability - Distributions/015 Daily-Views-post.xlsx 20.7 kB64 - Appendix - Working with Text Files in Python/022 Importing-Data-with-the-pandas-Squeeze-Method.ipynb 20.6 kB15 - Statistics - Descriptive Statistics/001 Glossary.xlsx 20.4 kB15 - Statistics - Descriptive Statistics/014 2.8.Skewness-exercise-solution.xlsx 20.2 kB51 - Deep Learning - Business Case Example/008 TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 20.2 kB36 - Advanced Statistical Methods - Logistic Regression/008 Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/011 Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/013 Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/016 Bank-data.csv 20.0 kB17 - Statistics - Inferential Statistics Fundamentals/002 3.2.What-is-a-distribution-lesson.xlsx 19.9 kB15 - Statistics - Descriptive Statistics/007 2.5.The-Histogram-lesson.xlsx 19.1 kB10 - Probability - Combinatorics/011 A Practical Example of Combinatorics_en.srt 19.0 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/001 Practical Example Linear Regression (Part 1)_en.srt 18.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb 18.4 kB64 - Appendix - Working with Text Files in Python/015 Importing Data with .loadtxt() and .genfromtxt()_en.srt 18.2 kB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps-with-comments.ipynb 18.1 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 TensorFlow-MNIST-around-98-percent-accuracy.ipynb 18.1 kB15 - Statistics - Descriptive Statistics/008 2.5.The-Histogram-exercise-solution.xlsx 17.5 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 17.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 SKLEAR-1.IPY 17.2 kB19 - Statistics - Practical Example Inferential Statistics/001 Practical Example Inferential Statistics_en.srt 17.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 TensorFlow-MNIST-All-Exercises.ipynb 17.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb 17.0 kB51 - Deep Learning - Business Case Example/004 Business Case Preprocessing the Data_en.srt 16.9 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Business Case Preprocessing_en.srt 16.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/017 sklearn-Feature-Scaling-Exercise-Solution.ipynb 16.7 kB15 - Statistics - Descriptive Statistics/010 2.6.Cross-table-and-scatter-plot-exercise.xlsx 16.7 kB64 - Appendix - Working with Text Files in Python/009 Importing Text Files - open()_en.srt 16.7 kB18 - Statistics - Inferential Statistics Confidence Intervals/006 3.11.The-t-table.xlsx 16.2 kB18 - Statistics - Inferential Statistics Confidence Intervals/007 3.11.The-t-table.xlsx 16.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 16.2 kB12 - Probability - Distributions/015 Customers-Membership-post.xlsx 16.0 kB15 - Statistics - Descriptive Statistics/008 2.5.The-Histogram-exercise.xlsx 15.9 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/010 TensorFlow-MNIST-Exercises-All.ipynb 15.8 kB62 - Appendix - Additional Python Tools/005 List Comprehensions_en.srt 15.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/013 sklearn-Multiple-Linear-Regression-Exercise-Solution.ipynb 15.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 15.7 kB38 - Advanced Statistical Methods - K-Means Clustering/015 Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.7 kB62 - Appendix - Additional Python Tools/001 Using the .format() Method_en.srt 15.6 kB15 - Statistics - Descriptive Statistics/004 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.6 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.6 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.4 kB02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI_en.srt 15.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2.ipynb 15.3 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 1.TensorFlow-MNIST-Width-Solution.ipynb 15.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 15.1 kB20 - Statistics - Hypothesis Testing/008 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.9 kB50 - Deep Learning - Classifying on the MNIST Dataset/012 TensorFlow-MNIST-complete-with-comments.ipynb 14.9 kB20 - Statistics - Hypothesis Testing/011 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.7 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/012 TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.7 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/006 Practical Example Linear Regression (Part 4)_en.srt 14.6 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.6 kB18 - Statistics - Inferential Statistics Confidence Intervals/010 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.6 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 14.5 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 14.4 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 1.TensorFlow-MNIST-Width-Solution.ipynb 14.3 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.3 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-All-Exercises.ipynb 14.3 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.3 kB05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods_en.srt 14.2 kB40 - Part 6 Mathematics/011 Why is Linear Algebra Useful_en.srt 14.2 kB18 - Statistics - Inferential Statistics Confidence Intervals/010 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 14.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 14.0 kB63 - Appendix - pandas Fundamentals/001 Location.csv 13.8 kB63 - Appendix - pandas Fundamentals/012 Location.csv 13.8 kB62 - Appendix - Additional Python Tools/001 Additional-Python-Tools-Lectures.ipynb 13.8 kB62 - Appendix - Additional Python Tools/006 Additional-Python-Tools-Lectures.ipynb 13.8 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5)_en.srt 13.7 kB64 - Appendix - Working with Text Files in Python/028 Saving-Data-NP-Solution.ipynb 13.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple-Linear-Regression-Exercise-Solution.ipynb 13.7 kB51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors_en.srt 13.7 kB02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction_en.srt 13.6 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset_en.srt 13.6 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)_en.srt 13.5 kB05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data_en.srt 13.5 kB15 - Statistics - Descriptive Statistics/006 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.5 kB56 - Software Integration/003 Taking a Closer Look at APIs_en.srt 13.4 kB63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series_en.srt 13.4 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 12.9.TensorFlow-MNIST-with-comments.ipynb 13.3 kB05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning_en.srt 13.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.3 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-All-Exercises.ipynb 13.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 SKLEAR-1.IPY 13.2 kB20 - Statistics - Hypothesis Testing/011 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 13.1 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/011 sklearn-How-to-properly-include-p-values.ipynb 13.0 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence_en.srt 13.0 kB63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames_en.srt 13.0 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature_en.srt 13.0 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning_en.srt 12.9 kB20 - Statistics - Hypothesis Testing/009 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.9 kB62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions_en.srt 12.9 kB15 - Statistics - Descriptive Statistics/018 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.9 kB12 - Probability - Distributions/002 Types of Probability Distributions_en.srt 12.9 kB28 - Python - Sequences/001 Lists_en.srt 12.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/010 TensorFlow-MNIST-Part6-with-comments.ipynb 12.8 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau_en.srt 12.6 kB13 - Probability - Probability in Other Fields/001 Probability in Finance_en.srt 12.5 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 5.6.TensorFlow-Minimal-example-complete.ipynb 12.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables_en.srt 12.3 kB17 - Statistics - Inferential Statistics Fundamentals/005 3.4.Standard-normal-distribution-exercise.xlsx 12.3 kB51 - Deep Learning - Business Case Example/011 TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB51 - Deep Learning - Business Case Example/012 TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering_en.srt 12.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 12.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained_en.srt 12.0 kB64 - Appendix - Working with Text Files in Python/016 Importing Data - Partial Cleaning While Importing Data_en.srt 12.0 kB36 - Advanced Statistical Methods - Logistic Regression/012 Accuracy-with-comments.ipynb 12.0 kB15 - Statistics - Descriptive Statistics/018 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.9 kB18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score_en.srt 11.9 kB64 - Appendix - Working with Text Files in Python/015 Importing-Text-Data-with-NumPy-Complete.ipynb 11.8 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau_en.srt 11.8 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch_en.srt 11.7 kB15 - Statistics - Descriptive Statistics/005 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Minimal-example-Part-4-Complete.ipynb 11.7 kB20 - Statistics - Hypothesis Testing/015 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.7 kB62 - Appendix - Additional Python Tools/001 Additional-Python-Tools-Exercises.ipynb 11.6 kB62 - Appendix - Additional Python Tools/006 Additional-Python-Tools-Exercises.ipynb 11.6 kB15 - Statistics - Descriptive Statistics/012 2.7.Mean-median-and-mode-exercise-solution.xlsx 11.6 kB20 - Statistics - Hypothesis Testing/009 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.6 kB20 - Statistics - Hypothesis Testing/013 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.5 kB05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques_en.srt 11.5 kB20 - Statistics - Hypothesis Testing/006 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.5 kB18 - Statistics - Inferential Statistics Confidence Intervals/002 3.9.Population-variance-known-z-score-lesson.xlsx 11.5 kB51 - Deep Learning - Business Case Example/004 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/012 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB18 - Statistics - Inferential Statistics Confidence Intervals/003 3.9.Population-variance-known-z-score-exercise-solution.xlsx 11.4 kB22 - Part 4 Introduction to Python/004 Installing Python and Jupyter_en.srt 11.4 kB64 - Appendix - Working with Text Files in Python/013 Importing .csv Files - Part III_en.srt 11.4 kB18 - Statistics - Inferential Statistics Confidence Intervals/007 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.4 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline_en.srt 11.3 kB03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML_en.srt 11.3 kB15 - Statistics - Descriptive Statistics/016 2.9.Variance-exercise-solution.xlsx 11.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)_en.srt 11.3 kB20 - Statistics - Hypothesis Testing/006 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.3 kB12 - Probability - Distributions/008 Characteristics of Continuous Distributions_en.srt 11.3 kB40 - Part 6 Mathematics/010 Dot Product of Matrices_en.srt 11.3 kB50 - Deep Learning - Classifying on the MNIST Dataset/009 TensorFlow-MNIST-Part5-with-comments.ipynb 11.2 kB56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_en.srt 11.2 kB38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)_en.srt 11.2 kB15 - Statistics - Descriptive Statistics/017 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.2 kB13 - Probability - Probability in Other Fields/002 Probability in Statistics_en.srt 11.2 kB20 - Statistics - Hypothesis Testing/005 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.2 kB15 - Statistics - Descriptive Statistics/012 2.7.Mean-median-and-mode-exercise.xlsx 11.1 kB18 - Statistics - Inferential Statistics Confidence Intervals/003 3.9.Population-variance-known-z-score-exercise.xlsx 11.1 kB15 - Statistics - Descriptive Statistics/016 2.9.Variance-exercise.xlsx 11.1 kB18 - Statistics - Inferential Statistics Confidence Intervals/006 3.11.Population-variance-unknown-t-score-lesson.xlsx 11.0 kB20 - Statistics - Hypothesis Testing/013 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11.0 kB38 - Advanced Statistical Methods - K-Means Clustering/015 Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 11.0 kB28 - Python - Sequences/005 Dictionaries_en.srt 11.0 kB09 - Part 2 Probability/001 The Basic Probability Formula_en.srt 10.9 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB64 - Appendix - Working with Text Files in Python/011 Importing .csv Files - Part I_en.srt 10.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set_en.srt 10.9 kB18 - Statistics - Inferential Statistics Confidence Intervals/007 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.9 kB42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent_en.srt 10.8 kB12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution_en.srt 10.8 kB20 - Statistics - Hypothesis Testing/015 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.8 kB05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques_en.srt 10.8 kB28 - Python - Sequences/002 Using Methods_en.srt 10.8 kB15 - Statistics - Descriptive Statistics/011 2.7.Mean-median-and-mode-lesson.xlsx 10.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/008 TensorFlow-MNIST-Part4-with-comments.ipynb 10.7 kB18 - Statistics - Inferential Statistics Confidence Intervals/009 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression_en.srt 10.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 sklearn-Feature-Selection-with-F-regression.ipynb 10.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.7 kB21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing_en.srt 10.6 kB17 - Statistics - Inferential Statistics Fundamentals/004 3.4.Standard-normal-distribution-lesson.xlsx 10.6 kB29 - Python - Iterations/003 Lists with the range() Function_en.srt 10.6 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.6 kB38 - Advanced Statistical Methods - K-Means Clustering/005 Categorical.csv 10.6 kB20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level_en.srt 10.6 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem_en.srt 10.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/009 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.6 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing_en.srt 10.6 kB63 - Appendix - pandas Fundamentals/001 Region.csv 10.5 kB63 - Appendix - pandas Fundamentals/012 Region.csv 10.5 kB62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops_en.srt 10.5 kB62 - Appendix - Additional Python Tools/004 Triple Nested For Loops_en.srt 10.5 kB44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2_en.srt 10.4 kB18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples_en.srt 10.4 kB18 - Statistics - Inferential Statistics Confidence Intervals/012 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.4 kB12 - Probability - Distributions/001 Fundamentals of Probability Distributions_en.srt 10.3 kB15 - Statistics - Descriptive Statistics/015 2.9.Variance-lesson.xlsx 10.3 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model_en.srt 10.3 kB51 - Deep Learning - Business Case Example/009 TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.3 kB51 - Deep Learning - Business Case Example/005 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]_en.srt 10.3 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2)_en.srt 10.2 kB64 - Appendix - Working with Text Files in Python/021 Importing Data in Python - an Important Exercise_en.srt 10.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python_en.srt 10.2 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python_en.srt 10.2 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider_en.srt 10.2 kB15 - Statistics - Descriptive Statistics/015 Variance_en.srt 10.1 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 MNIST Results and Testing_en.srt 10.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/009 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 10.1 kB64 - Appendix - Working with Text Files in Python/025 Saving-Data-NP-Complete.ipynb 10.1 kB18 - Statistics - Inferential Statistics Confidence Intervals/011 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 10.1 kB18 - Statistics - Inferential Statistics Confidence Intervals/012 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 10.1 kB18 - Statistics - Inferential Statistics Confidence Intervals/014 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 10.0 kB20 - Statistics - Hypothesis Testing/010 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known_en.srt 10.0 kB51 - Deep Learning - Business Case Example/009 Business Case Setting an Early Stopping Mechanism_en.srt 10.0 kB12 - Probability - Distributions/015 Customers-Membership.xlsx 9.9 kB60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.srt 9.9 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases_en.srt 9.9 kB06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science_en.srt 9.9 kB20 - Statistics - Hypothesis Testing/012 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II_en.srt 9.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the Date Column_en.srt 9.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning_en.srt 9.8 kB29 - Python - Iterations/004 Conditional Statements and Loops_en.srt 9.8 kB12 - Probability - Distributions/015 Daily-Views.xlsx 9.8 kB18 - Statistics - Inferential Statistics Confidence Intervals/013 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB15 - Statistics - Descriptive Statistics/014 2.8.Skewness-exercise.xlsx 9.7 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output_en.srt 9.7 kB22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks_en.srt 9.7 kB29 - Python - Iterations/006 How to Iterate over Dictionaries_en.srt 9.6 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions-with-comments.ipynb 9.6 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent_en.srt 9.5 kB20 - Statistics - Hypothesis Testing/014 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB23 - Python - Variables and Data Types/003 Python Strings_en.srt 9.5 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights_en.srt 9.5 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau_en.srt 9.5 kB11 - Probability - Bayesian Inference/011 Bayes' Law_en.srt 9.4 kB18 - Statistics - Inferential Statistics Confidence Intervals/014 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_1920x1080.m3u8 9.4 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_1024x576.m3u8 9.3 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_1280x720.m3u8 9.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_768x432.m3u8 9.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.srt 9.3 kB39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram_en.srt 9.3 kB64 - Appendix - Working with Text Files in Python/025 Saving Your Data with NumPy - Part I - .npy_en.srt 9.3 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_640x360.m3u8 9.3 kB38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters_en.srt 9.3 kB64 - Appendix - Working with Text Files in Python/018 Importing Data from .json Files_en.srt 9.3 kB38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)_en.srt 9.3 kB44 - Deep Learning - TensorFlow 2.0 Introduction/006 TensorFlow-Minimal-example-Part2.ipynb 9.3 kB64 - Appendix - Working with Text Files in Python/005 Importing Data in Python - Principles_en.srt 9.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB28 - Python - Sequences/004 Tuples_en.srt 9.2 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared_en.srt 9.2 kB02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many_en.srt 9.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression_en.srt 9.2 kB64 - Appendix - Working with Text Files in Python/010 Importing Text Files - with open()_en.srt 9.1 kB63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I_en.srt 9.0 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy_en.srt 9.0 kB22 - Part 4 Introduction to Python/001 Introduction to Programming_en.srt 8.9 kB12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution_en.srt 8.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I_en.srt 8.8 kB22 - Part 4 Introduction to Python/002 Why Python_en.srt 8.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/007 TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline_en.srt 8.8 kB51 - Deep Learning - Business Case Example/005 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model_en.srt 8.8 kB20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis_en.srt 8.8 kB13 - Probability - Probability in Other Fields/003 Probability in Data Science_en.srt 8.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity_en.srt 8.7 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set_en.srt 8.7 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB09 - Part 2 Probability/004 Events and Their Complements_en.srt 8.7 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB38 - Advanced Statistical Methods - K-Means Clustering/007 How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB30 - Python - Advanced Python Tools/001 Object Oriented Programming_en.srt 8.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)_en.srt 8.6 kB09 - Part 2 Probability/003 Frequency_en.srt 8.5 kB09 - Part 2 Probability/002 Computing Expected Values_en.srt 8.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization_en.srt 8.5 kB56 - Software Integration/005 Software Integration - Explained_en.srt 8.5 kB46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training_en.srt 8.5 kB36 - Advanced Statistical Methods - Logistic Regression/016 Bank-data-testing.csv 8.5 kB38 - Advanced Statistical Methods - K-Means Clustering/003 Countries-exercise.csv 8.5 kB38 - Advanced Statistical Methods - K-Means Clustering/007 Countries-exercise.csv 8.5 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net_en.srt 8.5 kB15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots_en.srt 8.5 kB64 - Appendix - Working with Text Files in Python/006 Plain Text Files, Flat Files and More_en.srt 8.4 kB26 - Python - Conditional Statements/003 The ELIF Statement_en.srt 8.4 kB01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course_en.srt 8.4 kB44 - Deep Learning - TensorFlow 2.0 Introduction/007 Interpreting the Result and Extracting the Weights and Bias_en.srt 8.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table_en.srt 8.3 kB20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples_en.srt 8.3 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared_en.srt 8.3 kB38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_en.srt 8.3 kB29 - Python - Iterations/001 For Loops_en.srt 8.3 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)_en.srt 8.2 kB15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques_en.srt 8.2 kB64 - Appendix - Working with Text Files in Python/026 Saving Your Data with NumPy - Part II - .npz_en.srt 8.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn_en.srt 8.2 kB38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering_en.srt 8.1 kB44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0_en.srt 8.1 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes_en.srt 8.0 kB36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model_en.srt 8.0 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created_en.srt 8.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression.ipynb 8.0 kB50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST Preprocess the Data - Create a Validation Set and Scale It_en.srt 8.0 kB04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines_en.srt 8.0 kB62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects_en.srt 7.9 kB18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error_en.srt 7.9 kB51 - Deep Learning - Business Case Example/008 Business Case Learning and Interpreting the Result_en.srt 7.8 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table_en.srt 7.8 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate_en.srt 7.8 kB15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation_en.srt 7.8 kB36 - Advanced Statistical Methods - Logistic Regression/015 Testing-the-model-with-comments.ipynb 7.7 kB23 - Python - Variables and Data Types/003 Strings-Lecture-Py3.ipynb 7.7 kB38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize_en.srt 7.7 kB56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses_en.srt 7.7 kB18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)_en.srt 7.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept_en.srt 7.7 kB52 - Deep Learning - Conclusion/004 An overview of CNNs_en.srt 7.7 kB37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters_en.srt 7.7 kB38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks_en.srt 7.7 kB11 - Probability - Bayesian Inference/004 Union of Sets_en.srt 7.6 kB64 - Appendix - Working with Text Files in Python/022 Customer-Gender.csv 7.6 kB40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices_en.srt 7.6 kB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps_en.srt 7.6 kB49 - Deep Learning - Preprocessing/003 Standardization_en.srt 7.6 kB38 - Advanced Statistical Methods - K-Means Clustering/014 Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB29 - Python - Iterations/002 While Loops and Incrementing_en.srt 7.5 kB10 - Probability - Combinatorics/006 Solving Combinations_en.srt 7.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split.ipynb 7.4 kB25 - Python - Other Python Operators/002 Logical and Identity Operators_en.srt 7.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence_en.srt 7.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets_en.srt 7.4 kB15 - Statistics - Descriptive Statistics/011 Mean, median and mode_en.srt 7.4 kB20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown_en.srt 7.3 kB11 - Probability - Bayesian Inference/007 The Conditional Probability Formula_en.srt 7.3 kB50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model_en.srt 7.3 kB64 - Appendix - Working with Text Files in Python/001 An Introduction to Working with Files in Python_en.srt 7.3 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module_en.srt 7.3 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dummy-variables-with-comments.ipynb 7.3 kB36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python_en.srt 7.2 kB17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution_en.srt 7.2 kB64 - Appendix - Working with Text Files in Python/008 Common Naming Conventions_en.srt 7.2 kB56 - Software Integration/004 Communication between Software Products through Text Files_en.srt 7.2 kB14 - Part 3 Statistics/001 Population and Sample_en.srt 7.2 kB63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()_en.srt 7.2 kB05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data_en.srt 7.1 kB46 - Deep Learning - Overfitting/001 What is Overfitting_en.srt 7.1 kB15 - Statistics - Descriptive Statistics/001 Types of Data_en.srt 7.1 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment_en.srt 7.1 kB52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches_en.srt 7.0 kB63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas_en.srt 7.0 kB12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution_en.srt 7.0 kB38 - Advanced Statistical Methods - K-Means Clustering/012 Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Minimal-example-Part-3.ipynb 7.0 kB36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Exercise.ipynb 7.0 kB50 - Deep Learning - Classifying on the MNIST Dataset/012 TensorFlow-MNIST-complete.ipynb 6.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity_en.srt 6.9 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )_en.srt 6.9 kB18 - Statistics - Inferential Statistics Confidence Intervals/004 Confidence Interval Clarifications_en.srt 6.9 kB36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables_en.srt 6.9 kB17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem_en.srt 6.8 kB40 - Part 6 Mathematics/008 Transpose of a Matrix_en.srt 6.8 kB63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[]_en.srt 6.8 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation_en.srt 6.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients_en.srt 6.8 kB64 - Appendix - Working with Text Files in Python/019 An Introduction to Working with Excel Files in Python_en.srt 6.8 kB57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise_en.srt 6.8 kB63 - Appendix - pandas Fundamentals/006 Using .sort_values()_en.srt 6.8 kB60 - Case Study - Loading the 'absenteeism_module'/001 absenteeism-module.py 6.8 kB28 - Python - Sequences/003 List Slicing_en.srt 6.8 kB20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error_en.srt 6.7 kB44 - Deep Learning - TensorFlow 2.0 Introduction/002 TensorFlow Outline and Comparison with Other Libraries_en.srt 6.7 kB08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions_en.srt 6.7 kB20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1)_en.srt 6.7 kB42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning_en.srt 6.7 kB01 - Part 1 Introduction/002 What Does the Course Cover_en.srt 6.7 kB11 - Probability - Bayesian Inference/001 Sets and Events_en.srt 6.7 kB52 - Deep Learning - Conclusion/001 Summary on What You've Learned_en.srt 6.7 kB12 - Probability - Distributions/005 Discrete Distributions The Bernoulli Distribution_en.srt 6.6 kB49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding_en.srt 6.6 kB20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)_en.srt 6.6 kB64 - Appendix - Working with Text Files in Python/027 Saving Your Data with NumPy - Part III - .csv_en.srt 6.6 kB18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score_en.srt 6.6 kB42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss_en.srt 6.6 kB12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution_en.srt 6.6 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework_en.srt 6.6 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model_en.srt 6.6 kB50 - Deep Learning - Classifying on the MNIST Dataset/005 TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model_en.srt 6.5 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/004 TensorFlow Intro_en.srt 6.5 kB20 - Statistics - Hypothesis Testing/007 p-value_en.srt 6.4 kB36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression_en.srt 6.4 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)_en.srt 6.4 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions_en.srt 6.4 kB17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution_en.srt 6.4 kB36 - Advanced Statistical Methods - Logistic Regression/005 Example-bank-data.csv 6.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python_en.srt 6.3 kB36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting_en.srt 6.3 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB28 - Python - Sequences/005 Dictionaries-Solution-Py3.ipynb 6.3 kB15 - Statistics - Descriptive Statistics/019 Covariance_en.srt 6.3 kB02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics_en.srt 6.3 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution_en.srt 6.2 kB39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering_en.srt 6.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/017 sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic_en.srt 6.2 kB36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function_en.srt 6.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB64 - Appendix - Working with Text Files in Python/003 Structured, Semi-Structured and Unstructured Data_en.srt 6.2 kB64 - Appendix - Working with Text Files in Python/028 Saving-Data-NP-Exercise.ipynb 6.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation_en.srt 6.1 kB46 - Deep Learning - Overfitting/003 What is Validation_en.srt 6.1 kB60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I_en.srt 6.1 kB15 - Statistics - Descriptive Statistics/021 Correlation Coefficient_en.srt 6.1 kB10 - Probability - Combinatorics/005 Solving Variations without Repetition_en.srt 6.1 kB37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis_en.srt 6.1 kB22 - Part 4 Introduction to Python/003 Why Jupyter_en.srt 6.1 kB38 - Advanced Statistical Methods - K-Means Clustering/011 Market-segmentation-example-with-comments.ipynb 6.0 kB30 - Python - Advanced Python Tools/004 Importing Modules in Python_en.srt 6.0 kB25 - Python - Other Python Operators/002 Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent_en.srt 6.0 kB38 - Advanced Statistical Methods - K-Means Clustering/002 Country-clusters-with-comments.ipynb 5.9 kB41 - Part 7 Deep Learning/001 What to Expect from this Part_en.srt 5.9 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent_en.srt 5.9 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset_en.srt 5.9 kB64 - Appendix - Working with Text Files in Python/023 Importing Files in Jupyter_en.srt 5.9 kB42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs_en.srt 5.9 kB23 - Python - Variables and Data Types/001 Variables_en.srt 5.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column_en.srt 5.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions.ipynb 5.9 kB36 - Advanced Statistical Methods - Logistic Regression/015 Testing-the-model.ipynb 5.9 kB15 - Statistics - Descriptive Statistics/002 Levels of Measurement_en.srt 5.9 kB64 - Appendix - Working with Text Files in Python/024 Saving Your Data with pandas_en.srt 5.9 kB42 - Deep Learning - Introduction to Neural Networks/002 Training the Model_en.srt 5.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/013 sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB64 - Appendix - Working with Text Files in Python/004 Text Files and Data Connectivity_en.srt 5.8 kB11 - Probability - Bayesian Inference/010 The Multiplication Law_en.srt 5.8 kB38 - Advanced Statistical Methods - K-Means Clustering/004 Categorical-data-with-comments.ipynb 5.8 kB18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)_en.srt 5.7 kB07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for_en.srt 5.7 kB51 - Deep Learning - Business Case Example/004 TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data_en.srt 5.7 kB38 - Advanced Statistical Methods - K-Means Clustering/007 How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB40 - Part 6 Mathematics/001 What is a Matrix_en.srt 5.7 kB27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity_en.srt 5.6 kB64 - Appendix - Working with Text Files in Python/022 Importing Data with the .squeeze() Method_en.srt 5.6 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several Straightforward Columns for this Exercise_en.srt 5.6 kB38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering_en.srt 5.6 kB11 - Probability - Bayesian Inference/002 Ways Sets Can Interact_en.srt 5.6 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/007 Backpropagation_en.srt 5.6 kB23 - Python - Variables and Data Types/003 Strings-Solution-Py3.ipynb 5.6 kB18 - Statistics - Inferential Statistics Confidence Intervals/005 Student's T Distribution_en.srt 5.6 kB27 - Python - Python Functions/002 How to Create a Function with a Parameter_en.srt 5.6 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1)_en.srt 5.5 kB36 - Advanced Statistical Methods - Logistic Regression/013 Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB10 - Probability - Combinatorics/007 Symmetry of Combinations_en.srt 5.5 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation_en.srt 5.5 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability_en.srt 5.5 kB35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3)_en.srt 5.5 kB12 - Probability - Distributions/013 Continuous Distributions The Exponential Distribution_en.srt 5.5 kB37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites_en.srt 5.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression_en.srt 5.4 kB36 - Advanced Statistical Methods - Logistic Regression/002 Admittance-with-comments.ipynb 5.4 kB15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table_en.srt 5.4 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)_en.srt 5.4 kB36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean_en.srt 5.4 kB10 - Probability - Combinatorics/002 Permutations and How to Use Them_en.srt 5.4 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset_en.srt 5.4 kB24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python_en.srt 5.4 kB40 - Part 6 Mathematics/009 Dot Product_en.srt 5.3 kB44 - Deep Learning - TensorFlow 2.0 Introduction/008 Customizing a TensorFlow 2 Model_en.srt 5.3 kB46 - Deep Learning - Overfitting/005 N-Fold Cross Validation_en.srt 5.3 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose_en.srt 5.3 kB57 - Case Study - What's Next in the Course/003 Introducing the Data Set_en.srt 5.3 kB27 - Python - Python Functions/007 Built-in Functions in Python_en.srt 5.3 kB51 - Deep Learning - Business Case Example/003 Business Case Balancing the Dataset_en.srt 5.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings_en.srt 5.3 kB40 - Part 6 Mathematics/006 Addition and Subtraction of Matrices_en.srt 5.2 kB36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model_en.srt 5.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data_en.srt 5.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn_en.srt 5.2 kB64 - Appendix - Working with Text Files in Python/002 File vs File Object, Reading vs Parsing Data_en.srt 5.2 kB10 - Probability - Combinatorics/009 Combinatorics in Real-Life The Lottery_en.srt 5.2 kB28 - Python - Sequences/003 List-Slicing-Lecture-Py3.ipynb 5.1 kB40 - Part 6 Mathematics/003 Linear Algebra and Geometry_en.srt 5.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression.ipynb 5.0 kB38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering-Categorical-Data-Solution.ipynb 5.0 kB57 - Case Study - What's Next in the Course/002 The Business Task_en.srt 5.0 kB49 - Deep Learning - Preprocessing/001 Preprocessing Introduction_en.srt 5.0 kB10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces_en.srt 5.0 kB44 - Deep Learning - TensorFlow 2.0 Introduction/003 TensorFlow 1 vs TensorFlow 2_en.srt 5.0 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python_en.srt 5.0 kB17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution_en.srt 5.0 kB40 - Part 6 Mathematics/002 Scalars and Vectors_en.srt 5.0 kB64 - Appendix - Working with Text Files in Python/014 Importing Data with index_col_en.srt 4.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB36 - Advanced Statistical Methods - Logistic Regression/008 Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates_en.srt 4.9 kB11 - Probability - Bayesian Inference/008 The Law of Total Probability_en.srt 4.9 kB63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II_en.srt 4.8 kB52 - Deep Learning - Conclusion/005 An Overview of RNNs_en.srt 4.8 kB38 - Advanced Statistical Methods - K-Means Clustering/012 Market-segmentation-example-Part2.ipynb 4.8 kB38 - Advanced Statistical Methods - K-Means Clustering/003 A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB30 - Python - Advanced Python Tools/003 What is the Standard Library_en.srt 4.7 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST_en.srt 4.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dummy-Variables.ipynb 4.7 kB51 - Deep Learning - Business Case Example/007 TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB28 - Python - Sequences/004 Tuples-Solution-Py3.ipynb 4.7 kB10 - Probability - Combinatorics/010 A Recap of Combinatorics_en.srt 4.7 kB64 - Appendix - Working with Text Files in Python/012 Importing .csv Files - Part II_en.srt 4.7 kB23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python_en.srt 4.7 kB40 - Part 6 Mathematics/005 What is a Tensor_en.srt 4.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_en.srt 4.7 kB40 - Part 6 Mathematics/004 Scalars-Vectors-and-Matrices.ipynb 4.7 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture_en.srt 4.6 kB38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters.ipynb 4.6 kB47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization_en.srt 4.6 kB27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB36 - Advanced Statistical Methods - Logistic Regression/011 Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting_en.srt 4.6 kB26 - Python - Conditional Statements/001 The IF Statement_en.srt 4.6 kB47 - Deep Learning - Initialization/002 Types of Simple Initializations_en.srt 4.6 kB10 - Probability - Combinatorics/004 Solving Variations with Repetition_en.srt 4.6 kB47 - Deep Learning - Initialization/001 What is Initialization_en.srt 4.6 kB38 - Advanced Statistical Methods - K-Means Clustering/014 Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB36 - Advanced Statistical Methods - Logistic Regression/005 Building-a-Logistic-Regression-Solution.ipynb 4.5 kB22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard_en.srt 4.5 kB15 - Statistics - Descriptive Statistics/013 Skewness_en.srt 4.5 kB42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version)_en.srt 4.5 kB28 - Python - Sequences/002 Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.5 kB37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering_en.srt 4.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset_en.srt 4.5 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages_en.srt 4.5 kB28 - Python - Sequences/005 Dictionaries-Lecture-Py3.ipynb 4.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST_en.srt 4.4 kB27 - Python - Python Functions/005 Conditional Statements and Functions_en.srt 4.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter_en.srt 4.4 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression_en.srt 4.4 kB05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods_en.srt 4.4 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset_en.srt 4.4 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum_en.srt 4.4 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm_en.srt 4.4 kB28 - Python - Sequences/003 List-Slicing-Solution-Py3.ipynb 4.4 kB24 - Python - Basic Python Syntax/001 Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression_en.srt 4.3 kB64 - Appendix - Working with Text Files in Python/017 Importing-Text-Data-DSc-Exercise.ipynb 4.3 kB44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow_en.srt 4.2 kB11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets_en.srt 4.2 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression_en.srt 4.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors_en.srt 4.2 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)_en.srt 4.2 kB36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression-tables-fixed-error.ipynb 4.2 kB46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets_en.srt 4.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/006 Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.2 kB10 - Probability - Combinatorics/003 Simple Operations with Factorials_en.srt 4.2 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 Simple-linear-regression-with-comments.ipynb 4.2 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1_en.srt 4.2 kB15 - Statistics - Descriptive Statistics/007 The Histogram_en.srt 4.1 kB38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data_en.srt 4.1 kB50 - Deep Learning - Classifying on the MNIST Dataset/003 TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB26 - Python - Conditional Statements/002 The ELSE Statement_en.srt 4.0 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 What is a Deep Net_en.srt 4.0 kB18 - Statistics - Inferential Statistics Confidence Intervals/001 What are Confidence Intervals_en.srt 3.9 kB12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution_en.srt 3.9 kB38 - Advanced Statistical Methods - K-Means Clustering/011 Market-segmentation-example.ipynb 3.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 Simple-linear-regression.ipynb 3.9 kB23 - Python - Variables and Data Types/001 Variables-Solution-Py3.ipynb 3.9 kB38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering-Categorical-Data-Exercise.ipynb 3.9 kB36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip_en.srt 3.8 kB26 - Python - Conditional Statements/004 A Note on Boolean Values_en.srt 3.8 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 Business Case Interpretation_en.srt 3.8 kB27 - Python - Python Functions/003 Defining a Function in Python - Part II_en.srt 3.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values_en.srt 3.8 kB27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Minimal-example-Part-2.ipynb 3.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions_en.srt 3.7 kB36 - Advanced Statistical Methods - Logistic Regression/012 Accuracy.ipynb 3.7 kB38 - Advanced Statistical Methods - K-Means Clustering/015 iris-with-answers.csv 3.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST Importing the Relevant Packages and Loading the Data_en.srt 3.7 kB38 - Advanced Statistical Methods - K-Means Clustering/003 A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB23 - Python - Variables and Data Types/001 Variables-Lecture-Py3.ipynb 3.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_en.srt 3.7 kB40 - Part 6 Mathematics/010 Dot-product-Part-2.ipynb 3.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer_en.srt 3.7 kB05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML)_en.srt 3.7 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise_en.srt 3.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent_en.srt 3.6 kB36 - Advanced Statistical Methods - Logistic Regression/002 Admittance.ipynb 3.6 kB12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution_en.srt 3.6 kB24 - Python - Basic Python Syntax/001 Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss_en.srt 3.6 kB12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution_en.srt 3.5 kB25 - Python - Other Python Operators/002 Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification_en.srt 3.5 kB49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data_en.srt 3.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 real-estate-price-size-year-view.csv 3.5 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping_en.srt 3.5 kB23 - Python - Variables and Data Types/002 Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB64 - Appendix - Working with Text Files in Python/020 Working with Excel (.xlsx) Data_en.srt 3.4 kB42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs_en.srt 3.4 kB38 - Advanced Statistical Methods - K-Means Clustering/004 Categorical-data.ipynb 3.4 kB38 - Advanced Statistical Methods - K-Means Clustering/002 Country-clusters.ipynb 3.4 kB27 - Python - Python Functions/003 Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.4 kB11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets_en.srt 3.4 kB26 - Python - Conditional Statements/003 Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.3 kB40 - Part 6 Mathematics/006 Adding-and-subtracting-matrices.ipynb 3.3 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model_en.srt 3.3 kB42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks_en.srt 3.3 kB40 - Part 6 Mathematics/007 Errors when Adding Matrices_en.srt 3.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section_en.srt 3.3 kB52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning_en.srt 3.3 kB45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer_en.srt 3.3 kB28 - Python - Sequences/001 Lists-Solution-Py3.ipynb 3.3 kB64 - Appendix - Working with Text Files in Python/025 Saving-Data-NP-Template.ipynb 3.2 kB40 - Part 6 Mathematics/007 Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 kB36 - Advanced Statistical Methods - Logistic Regression/008 Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 kB27 - Python - Python Functions/001 Defining a Function in Python_en.srt 3.2 kB11 - Probability - Bayesian Inference/009 The Additive Rule_en.srt 3.2 kB55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution_en.srt 3.2 kB65 - Bonus Lecture/001 Bonus Lecture Next Steps.html 3.2 kB25 - Python - Other Python Operators/001 Comparison Operators_en.srt 3.2 kB24 - Python - Basic Python Syntax/003 Reassign-Values-Lecture-Py3.ipynb 3.2 kB11 - Probability - Bayesian Inference/003 Intersection of Sets_en.srt 3.1 kB12 - Probability - Distributions/003 Characteristics of Discrete Distributions_en.srt 3.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1 Linearity_en.srt 3.1 kB29 - Python - Iterations/005 Conditional Statements, Functions, and Loops_en.srt 3.0 kB29 - Python - Iterations/004 Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test)_en.srt 3.0 kB28 - Python - Sequences/005 Dictionaries-Exercise-Py3.ipynb 3.0 kB36 - Advanced Statistical Methods - Logistic Regression/005 Building-a-Logistic-Regression-Exercise.ipynb 3.0 kB28 - Python - Sequences/004 Tuples-Lecture-Py3.ipynb 3.0 kB40 - Part 6 Mathematics/008 Tranpose-of-a-matrix.ipynb 3.0 kB29 - Python - Iterations/006 Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 kB28 - Python - Sequences/002 Help-Yourself-with-Methods-Solution-Py3.ipynb 2.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/005 What's Regression Analysis - a Quick Refresher.html 2.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.9 kB05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data_en.srt 2.9 kB28 - Python - Sequences/003 List-Slicing-Exercise-Py3.ipynb 2.9 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow_en.srt 2.9 kB31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis_en.srt 2.8 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 Simple-Linear-Regression-Exercise.ipynb 2.8 kB24 - Python - Basic Python Syntax/007 Structuring with Indentation_en.srt 2.8 kB38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression_en.srt 2.8 kB64 - Appendix - Working with Text Files in Python/007 Text Files of Fixed Width_en.srt 2.8 kB42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_en.srt 2.8 kB28 - Python - Sequences/001 Lists-Lecture-Py3.ipynb 2.8 kB05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI)_en.srt 2.7 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized_en.srt 2.7 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118.m3u8 2.7 kB24 - Python - Basic Python Syntax/001 Arithmetic-Operators-Exercise-Py3.ipynb 2.7 kB23 - Python - Variables and Data Types/003 Strings-Exercise-Py3.ipynb 2.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression_en.srt 2.7 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages_en.srt 2.7 kB36 - Advanced Statistical Methods - Logistic Regression/010 2.02.Binary-predictors.csv 2.6 kB51 - Deep Learning - Business Case Example/011 Business Case Testing the Model_en.srt 2.6 kB36 - Advanced Statistical Methods - Logistic Regression/011 Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.6 kB25 - Python - Other Python Operators/001 Comparison-Operators-Lecture-Py3.ipynb 2.6 kB27 - Python - Python Functions/004 How to Use a Function within a Function_en.srt 2.6 kB17 - Statistics - Inferential Statistics Fundamentals/007 Standard error_en.srt 2.6 kB36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression-summary-error.ipynb 2.5 kB64 - Appendix - Working with Text Files in Python/010 Importing-Text-Files-in-Python-with-open.ipynb 2.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple-Linear-Regression-Exercise.ipynb 2.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/001 What to Expect from the Following Sections.html 2.5 kB18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3)_en.srt 2.5 kB36 - Advanced Statistical Methods - Logistic Regression/010 Binary-predictors.ipynb 2.5 kB25 - Python - Other Python Operators/001 Comparison-Operators-Solution-Py3.ipynb 2.5 kB38 - Advanced Statistical Methods - K-Means Clustering/014 iris-dataset.csv 2.5 kB38 - Advanced Statistical Methods - K-Means Clustering/015 iris-dataset.csv 2.5 kB26 - Python - Conditional Statements/003 Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/013 real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/017 real-estate-price-size-year.csv 2.4 kB24 - Python - Basic Python Syntax/004 Add Comments_en.srt 2.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/014 Dropping a Dummy Variable from the Data Set.html 2.4 kB24 - Python - Basic Python Syntax/002 The Double Equality Sign_en.srt 2.3 kB23 - Python - Variables and Data Types/002 Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/003 A Note on Installing Packages in Anaconda.html 2.3 kB64 - Appendix - Working with Text Files in Python/015 Importing-Text-Data-with-NumPy-Template.ipynb 2.3 kB51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution_en.srt 2.3 kB29 - Python - Iterations/003 Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 kB05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data_en.srt 2.3 kB20 - Statistics - Hypothesis Testing/002 Further Reading on Null and Alternative Hypothesis.html 2.3 kB23 - Python - Variables and Data Types/001 Variables-Exercise-Py3.ipynb 2.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python_en.srt 2.3 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 MNIST Solutions.html 2.3 kB49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing_en.srt 2.3 kB26 - Python - Conditional Statements/001 Introduction-to-the-If-Statement-Solution-Py3.ipynb 2.2 kB29 - Python - Iterations/006 Iterating-over-Dictionaries-Exercise-Py3.ipynb 2.2 kB24 - Python - Basic Python Syntax/006 Indexing-Elements-Solution-Py3.ipynb 2.2 kB36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression_en.srt 2.2 kB54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/010 MNIST Exercises.html 2.2 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Multiple-linear-regression-and-Adjusted-R-squared.ipynb 2.2 kB64 - Appendix - Working with Text Files in Python/009 Importing-Text-Files-in-Python-open.ipynb 2.2 kB28 - Python - Sequences/001 Lists-Exercise-Py3.ipynb 2.2 kB40 - Part 6 Mathematics/009 Dot-product.ipynb 2.2 kB24 - Python - Basic Python Syntax/003 Reassign-Values-Solution-Py3.ipynb 2.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/014 ARTICLE - A Note on 'pickling'.html 2.2 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/001 Absenteeism-predictions.csv 2.2 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Absenteeism-predictions.csv 2.2 kB29 - Python - Iterations/004 Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb 2.1 kB36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression.ipynb 2.1 kB40 - Part 6 Mathematics/005 Tensors.ipynb 2.1 kB28 - Python - Sequences/004 Tuples-Exercise-Py3.ipynb 2.1 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model_en.srt 2.1 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables A Statistical Perspective_en.srt 2.1 kB24 - Python - Basic Python Syntax/006 Indexing Elements_en.srt 2.0 kB27 - Python - Python Functions/003 Another-Way-to-Define-a-Function-Solution-Py3.ipynb 2.0 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 MNIST - Exercises.html 2.0 kB17 - Statistics - Inferential Statistics Fundamentals/001 Introduction_en.srt 2.0 kB29 - Python - Iterations/004 Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb 2.0 kB28 - Python - Sequences/002 Help-Yourself-with-Methods-Exercise-Py3.ipynb 2.0 kB29 - Python - Iterations/005 All-In-Solution-Py3.ipynb 1.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs_en.srt 1.9 kB60 - Case Study - Loading the 'absenteeism_module'/001 Absenteeism-new-data.csv 1.9 kB60 - Case Study - Loading the 'absenteeism_module'/001 scaler 1.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 real-estate-price-size.csv 1.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/006 real-estate-price-size.csv 1.9 kB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.ipynb 1.9 kB29 - Python - Iterations/001 For-Loops-Solution-Py3.ipynb 1.8 kB27 - Python - Python Functions/002 Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb 1.8 kB26 - Python - Conditional Statements/002 Add-an-Else-Statement-Lecture-Py3.ipynb 1.8 kB27 - Python - Python Functions/006 Functions Containing a Few Arguments_en.srt 1.8 kB26 - Python - Conditional Statements/003 Else-If-for-Brief-Elif-Exercise-Py3.ipynb 1.8 kB29 - Python - Iterations/002 While-Loops-and-Incrementing-Solution-Py3.ipynb 1.8 kB10 - Probability - Combinatorics/001 Fundamentals of Combinatorics_en.srt 1.8 kB30 - Python - Advanced Python Tools/002 Modules and Packages_en.srt 1.8 kB27 - Python - Python Functions/006 Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb 1.8 kB44 - Deep Learning - TensorFlow 2.0 Introduction/004 A Note on TensorFlow 2 Syntax_en.srt 1.7 kB24 - Python - Basic Python Syntax/003 Reassign-Values-Exercise-Py3.ipynb 1.7 kB44 - Deep Learning - TensorFlow 2.0 Introduction/005 TensorFlow-Minimal-example-Part1.ipynb 1.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Basic NN Example Exercises.html 1.7 kB27 - Python - Python Functions/005 Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb 1.7 kB29 - Python - Iterations/005 All-In-Lecture-Py3.ipynb 1.7 kB25 - Python - Other Python Operators/001 Comparison-Operators-Exercise-Py3.ipynb 1.6 kB27 - Python - Python Functions/004 0.6.4-Using-a-Function-in-another-Function-Solution-Py3.ipynb 1.6 kB27 - Python - Python Functions/002 Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb 1.6 kB36 - Advanced Statistical Methods - Logistic Regression/002 2.01.Admittance.csv 1.6 kB53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 Basic NN Example with TF Exercises.html 1.6 kB24 - Python - Basic Python Syntax/003 How to Reassign Values_en.srt 1.6 kB64 - Appendix - Working with Text Files in Python/011 Importing.csv-Files-with-pandas-Part-I.ipynb 1.6 kB26 - Python - Conditional Statements/001 Introduction-to-the-If-Statement-Exercise-Py3.ipynb 1.6 kB24 - Python - Basic Python Syntax/005 Line-Continuation-Solution-Py3.ipynb 1.5 kB24 - Python - Basic Python Syntax/007 Structure-Your-Code-with-Indentation-Solution-Py3.ipynb 1.5 kB29 - Python - Iterations/003 Create-Lists-with-the-range-Function-Exercise-Py3.ipynb 1.5 kB24 - Python - Basic Python Syntax/002 The-Double-Equality-Sign-Lecture-Py3.ipynb 1.5 kB26 - Python - Conditional Statements/002 Add-an-Else-Statement-Solution-Py3.ipynb 1.4 kB24 - Python - Basic Python Syntax/005 Understanding Line Continuation_en.srt 1.4 kB24 - Python - Basic Python Syntax/006 Indexing-Elements-Exercise-Py3.ipynb 1.4 kB64 - Appendix - Working with Text Files in Python/029 Working with Text Files in Python - Conclusion_en.srt 1.4 kB29 - Python - Iterations/003 Create-Lists-with-the-range-Function-Lecture-Py3.ipynb 1.4 kB24 - Python - Basic Python Syntax/006 Indexing-Elements-Lecture-Py3.ipynb 1.3 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 First Regression in Python Exercise.html 1.3 kB29 - Python - Iterations/005 All-In-Exercise-Py3.ipynb 1.3 kB27 - Python - Python Functions/005 Combining-Conditional-Statements-and-Functions-Lecture-Py3.ipynb 1.3 kB44 - Deep Learning - TensorFlow 2.0 Introduction/009 Basic NN with TensorFlow Exercises.html 1.3 kB29 - Python - Iterations/001 For-Loops-Exercise-Py3.ipynb 1.3 kB29 - Python - Iterations/001 For-Loops-Lecture-Py3.ipynb 1.3 kB27 - Python - Python Functions/003 Another-Way-to-Define-a-Function-Exercise-Py3.ipynb 1.3 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 1.03.Dummies.csv 1.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Minimal-example-Part-1.ipynb 1.2 kB27 - Python - Python Functions/002 Creating-a-Function-with-a-Parameter-Exercise-Py3.ipynb 1.2 kB26 - Python - Conditional Statements/001 Introduction-to-the-If-Statement-Lecture-Py3.ipynb 1.2 kB24 - Python - Basic Python Syntax/002 The-Double-Equality-Sign-Solution-Py3.ipynb 1.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/029 EXERCISE - Removing the Date Column.html 1.2 kB24 - Python - Basic Python Syntax/005 Line-Continuation-Exercise-Py3.ipynb 1.2 kB29 - Python - Iterations/002 While-Loops-and-Incrementing-Exercise-Py3.ipynb 1.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 1.02.Multiple-linear-regression.csv 1.1 kB29 - Python - Iterations/002 While-Loops-and-Incrementing-Lecture-Py3.ipynb 1.1 kB29 - Python - Iterations/006 Iterating-over-Dictionaries-Lecture-Py3.ipynb 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/009 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/011 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 1.02.Multiple-linear-regression.csv 1.1 kB27 - Python - Python Functions/005 Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb 1.1 kB27 - Python - Python Functions/004 0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb 1.1 kB52 - Deep Learning - Conclusion/003 DeepMind and Deep Learning.html 1.1 kB24 - Python - Basic Python Syntax/004 Add-Comments-Lecture-Py3.ipynb 1.1 kB26 - Python - Conditional Statements/002 Add-an-Else-Statement-Exercise-Py3.ipynb 1.0 kB60 - Case Study - Loading the 'absenteeism_module'/001 model 1.0 kB27 - Python - Python Functions/004 0.6.4-Using-a-Function-in-another-Function-Lecture-Py3.ipynb 1.0 kB60 - Case Study - Loading the 'absenteeism_module'/004 Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb 973 Bytes60 - Case Study - Loading the 'absenteeism_module'/004 Exporting the Obtained Data Set as a .csv.html 964 Bytes24 - Python - Basic Python Syntax/007 Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb 958 Bytes24 - Python - Basic Python Syntax/007 Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb 956 Bytes32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/003 1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/004 1.01.Simple-linear-regression.csv 922 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/033 A Note on Exporting Your Data as a .csv File.html 880 Bytes27 - Python - Python Functions/001 Defining-a-Function-in-Python-Lecture-Py3.ipynb 868 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/008 EXERCISE - Dropping a Column from a DataFrame in Python.html 864 Bytes35 - Advanced Statistical Methods - Practical Example Linear Regression/003 A Note on Multicollinearity.html 849 Bytes24 - Python - Basic Python Syntax/002 The-Double-Equality-Sign-Exercise-Py3.ipynb 838 Bytes26 - Python - Conditional Statements/004 A-Note-on-Boolean-Values-Lecture-Py3.ipynb 791 Bytes
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-links.txt 790 Bytes24 - Python - Basic Python Syntax/005 Line-Continuation-Lecture-Py3.ipynb 779 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/005 A Note on Normalization.html 729 Bytes35 - Advanced Statistical Methods - Practical Example Linear Regression/007 Dummy Variables - Exercise.html 705 Bytes53 - Appendix Deep Learning - TensorFlow 1 Introduction/001 READ ME!!!!.html 564 Bytes45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/009 Backpropagation - A Peek into the Mathematics of Optimization.html 539 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/005 EXERCISE - Transportation Expense vs Probability.html 529 Bytes15 - Statistics - Descriptive Statistics/016 Variance Exercise.html 522 Bytes60 - Case Study - Loading the 'absenteeism_module'/001 Are You Sure You're All Set.html 513 Bytes35 - Advanced Statistical Methods - Practical Example Linear Regression/009 Linear Regression - Exercise.html 497 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/022 SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 478 Bytes55 - Appendix Deep Learning - TensorFlow 1 Business Case/012 Business Case Final Exercise.html 441 Bytes51 - Deep Learning - Business Case Example/012 Business Case Final Exercise.html 433 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/003 EXERCISE - Reasons vs Probability.html 385 Bytes55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 Business Case Preprocessing Exercise.html 379 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/011 A Note on Calculation of P-values with sklearn.html 370 Bytes51 - Deep Learning - Business Case Example/005 Business Case Preprocessing the Data - Exercise.html 370 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/001 EXERCISE - Age vs Probability.html 367 Bytes36 - Advanced Statistical Methods - Logistic Regression/015 2.03.Test-dataset.csv 322 Bytes64 - Appendix - Working with Text Files in Python/017 Importing Data with NumPy - Exercise.html 308 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/015 EXERCISE - Saving the Model (and Scaler).html 284 Bytes38 - Advanced Statistical Methods - K-Means Clustering/011 3.12.Example.csv 283 Bytes64 - Appendix - Working with Text Files in Python/028 Saving Data with Numpy - Exercise.html 260 Bytes39 - Advanced Statistical Methods - Other Types of Clustering/003 Country-clusters-standardized.csv 244 Bytes38 - Advanced Statistical Methods - K-Means Clustering/002 3.01.Country-clusters.csv 200 Bytes51 - Deep Learning - Business Case Example/010 Setting an Early Stopping Mechanism - Exercise.html 192 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/018 EXERCISE - Using .concat() in Python.html 189 Bytes
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Logistic-Regression-prior-to-Backward-Elimination.url 189 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Logistic-Regression-prior-to-Custom-Scaler.url 182 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/015 Logistic-Regression-with-Comments.url 173 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/021 EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 161 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/015 Logistic-Regression.url 159 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/019 SOLUTION - Using .concat() in Python.html 143 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/024 EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 Bytes35 - Advanced Statistical Methods - Practical Example Linear Regression/external-links.txt 134 Bytes0. Websites you may like/[FreeCourseSite.com].url 127 Bytes03 - The Field of Data Science - Connecting the Data Science Disciplines/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes21 - Statistics - Practical Example Hypothesis Testing/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes30 - Python - Advanced Python Tools/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes64 - Appendix - Working with Text Files in Python/external-links.txt 124 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/012 EXERCISE - Obtaining Dummies from a Single Feature.html 123 Bytes0. Websites you may like/[CourseClub.Me].url 122 Bytes03 - The Field of Data Science - Connecting the Data Science Disciplines/0. Websites you may like/[CourseClub.Me].url 122 Bytes21 - Statistics - Practical Example Hypothesis Testing/0. Websites you may like/[CourseClub.Me].url 122 Bytes30 - Python - Advanced Python Tools/0. Websites you may like/[CourseClub.Me].url 122 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/025 SOLUTION - Creating Checkpoints while Coding in Jupyter.html 118 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/013 SOLUTION - Obtaining Dummies from a Single Feature.html 117 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/009 SOLUTION - Dropping a Column from a DataFrame in Python.html 114 Bytes01 - Part 1 Introduction/external-links.txt 105 Bytes01 - Part 1 Introduction/003 Download-all-resources.url 97 Bytes35 - Advanced Statistical Methods - Practical Example Linear Regression/004 sklearn-Linear-Regression-Practical-Example-Part-3-.url 97 Bytes64 - Appendix - Working with Text Files in Python/001 Section-Resources-Working-with-Text-Files.url 97 Bytes36 - Advanced Statistical Methods - Logistic Regression/005 Building a Logistic Regression - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/008 Understanding Logistic Regression Tables - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/011 Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/013 Calculating the Accuracy of the Model.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/016 Testing the Model - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/003 A Simple Example of Clustering - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering Categorical Data - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/007 How to Choose the Number of Clusters - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/014 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/015 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes15 - Statistics - Descriptive Statistics/004 Categorical Variables Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/006 Numerical Variables Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/008 Histogram Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/010 Cross Tables and Scatter Plots Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/012 Mean, Median and Mode Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/014 Skewness Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/018 Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/020 Covariance Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/022 Correlation Coefficient Exercise.html 81 Bytes16 - Statistics - Practical Example Descriptive Statistics/002 Practical Example Descriptive Statistics Exercise.html 81 Bytes17 - Statistics - Inferential Statistics Fundamentals/005 The Standard Normal Distribution Exercise.html 81 Bytes18 - Statistics - Inferential Statistics Confidence Intervals/003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 Bytes18 - Statistics - Inferential Statistics Confidence Intervals/007 Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 Bytes18 - Statistics - Inferential Statistics Confidence Intervals/010 Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes18 - Statistics - Inferential Statistics Confidence Intervals/012 Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 Bytes18 - Statistics - Inferential Statistics Confidence Intervals/014 Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 Bytes19 - Statistics - Practical Example Inferential Statistics/002 Practical Example Inferential Statistics Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/006 Test for the Mean. Population Variance Known Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/009 Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/011 Test for the Mean. Dependent Samples Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/013 Test for the mean. Independent Samples (Part 1). Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/015 Test for the mean. Independent Samples (Part 2). 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Websites you may like/[GigaCourse.Com].url 49 Bytes03 - The Field of Data Science - Connecting the Data Science Disciplines/0. Websites you may like/[GigaCourse.Com].url 49 Bytes21 - Statistics - Practical Example Hypothesis Testing/0. Websites you may like/[GigaCourse.Com].url 49 Bytes30 - Python - Advanced Python Tools/0. Websites you may like/[GigaCourse.Com].url 49 Bytes64 - Appendix - Working with Text Files in Python/009 source.txt 39 Bytes64 - Appendix - Working with Text Files in Python/010 source.txt 39 Bytes06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.m4a.part 0 Bytes06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.mp4.part 0 Bytes33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.encrypted.m4a.part 0 Bytes33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.encrypted.mp4.part 0 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.encrypted.m4a.part 0 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.encrypted.mp4.part 0 Bytes
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