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11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 168.2 MB33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4 151.3 MB5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 145.0 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.vtt 145.0 MB3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 133.0 MB5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 131.2 MB5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 129.5 MB46. Software Integration/5. Taking a Closer Look at APIs.mp4 121.2 MB15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 117.7 MB2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 114.3 MB46. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 109.1 MB6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 108.5 MB44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 108.4 MB14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 107.7 MB5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 104.2 MB15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 96.6 MB5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 94.3 MB44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 91.9 MB29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 90.7 MB2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 85.4 MB4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 85.1 MB48. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 85.0 MB13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 82.0 MB44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 80.1 MB5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 79.2 MB17. Part 3 Introduction to Python/3. Why Python.mp4 78.7 MB48. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 78.2 MB31. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 78.1 MB8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 76.4 MB46. Software Integration/9. Software Integration - Explained.mp4 76.2 MB10. Statistics - Descriptive Statistics/1. Types of Data.mp4 76.0 MB30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 75.0 MB13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4 73.9 MB16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 72.9 MB46. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 72.4 MB2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 71.0 MB2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 67.7 MB12. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 65.9 MB43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 65.8 MB1. Part 1 Introduction/2. What Does the Course Cover.mp4 65.3 MB48. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 64.9 MB48. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 64.8 MB12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 64.6 MB36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 64.1 MB46. Software Integration/7. Communication between Software Products through Text Files.mp4 63.3 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 62.2 MB51. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB13. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4 62.0 MB45. Deep Learning - Conclusion/3. An overview of CNNs.mp4 61.6 MB17. Part 3 Introduction to Python/1. Introduction to Programming.mp4 61.4 MB9. Part 2 Statistics/1. Population and Sample.mp4 60.9 MB27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4 60.2 MB48. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 60.1 MB13. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4 59.8 MB51. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 59.3 MB43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 59.1 MB31. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 58.8 MB15. Statistics - Hypothesis Testing/10. p-value.mp4 58.6 MB28. Advanced Statistical Methods - Multiple Linear Regression/18. Dealing with Categorical Data - Dummy Variables.mp4 58.4 MB35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 58.3 MB28. Advanced Statistical Methods - Multiple Linear Regression/3. Adjusted R-Squared.mp4 57.5 MB17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4 57.1 MB10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 57.0 MB7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 57.0 MB50. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 56.9 MB15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 56.9 MB2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 56.2 MB30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 56.0 MB44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 55.7 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4 55.3 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4 54.9 MB47. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 54.8 MB31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 54.3 MB42. Deep Learning - Preprocessing/3. Standardization.mp4 53.5 MB10. Statistics - Descriptive Statistics/22. Variance.mp4 53.4 MB15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 52.8 MB13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 52.4 MB12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 52.3 MB33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp4 52.2 MB27. Advanced Statistical Methods - Linear regression/13. Decomposition of Variability.mp4 52.1 MB33. Part 5 Mathematics/15. Dot Product of Matrices.mp4 51.8 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 51.4 MB1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 51.4 MB12. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 50.2 MB48. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 50.1 MB37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 50.0 MB43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 49.0 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4 48.0 MB10. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 47.3 MB35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 47.3 MB45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 46.9 MB27. Advanced Statistical Methods - Linear regression/11. How to Interpret the Regression Table.mp4 46.8 MB32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 46.7 MB27. Advanced Statistical Methods - Linear regression/8. First Regression in Python.mp4 46.7 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 46.6 MB17. Part 3 Introduction to Python/5. Why Jupyter.mp4 46.5 MB31. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 46.3 MB15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 46.1 MB43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 46.0 MB31. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 45.1 MB35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 45.0 MB5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 44.9 MB28. Advanced Statistical Methods - Multiple Linear Regression/13. A3 Normality and Homoscedasticity.mp4 44.8 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4 43.6 MB44. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 43.5 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 43.2 MB27. Advanced Statistical Methods - Linear regression/17. R-Squared.mp4 43.0 MB47. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 42.9 MB51. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 42.6 MB27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.mp4 42.6 MB48. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 42.5 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 42.4 MB15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 42.2 MB10. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 41.7 MB45. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 41.7 MB48. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 41.5 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 41.5 MB35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 41.3 MB44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 41.3 MB47. Case Study - What's Next in the Course/2. The Business Task.mp4 41.1 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB48. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 40.6 MB37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 40.4 MB10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 40.3 MB29. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 40.3 MB35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 40.2 MB33. Part 5 Mathematics/13. Transpose of a Matrix.mp4 39.9 MB31. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 39.5 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 39.3 MB37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 39.2 MB35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 39.0 MB10. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 38.9 MB5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 38.6 MB44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 38.1 MB15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4 38.1 MB30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 37.9 MB28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.mp4 37.4 MB13. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4 37.2 MB36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 36.6 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 36.6 MB29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 36.4 MB33. Part 5 Mathematics/3. Scalars and Vectors.mp4 35.5 MB33. Part 5 Mathematics/1. What is a matrix.mp4 35.2 MB25. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 35.2 MB21. Python - Conditional Statements/4. The ELIF Statement.mp4 34.8 MB29. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 34.5 MB39. Deep Learning - Overfitting/3. What is Validation.mp4 34.3 MB33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp4 34.2 MB37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 34.1 MB29. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 33.8 MB29. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 33.8 MB13. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4 33.8 MB28. Advanced Statistical Methods - Multiple Linear Regression/14. A4 No Autocorrelation.mp4 33.1 MB27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.vtt 32.9 MB34. Part 6 Deep Learning/1. What to Expect from this Part.mp4 32.6 MB39. Deep Learning - Overfitting/1. What is Overfitting.mp4 32.6 MB23. Python - Sequences/5. List Slicing.mp4 32.3 MB18. Python - Variables and Data Types/5. Python Strings.mp4 32.3 MB17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 32.1 MB29. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 32.0 MB31. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 31.6 MB20. Python - Other Python Operators/3. Logical and Identity Operators.mp4 31.5 MB15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 31.4 MB5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 31.4 MB32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 31.1 MB5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 31.0 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 31.0 MB48. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 30.9 MB10. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 30.8 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 30.5 MB32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 30.5 MB42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 30.4 MB13. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4 30.2 MB35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 30.1 MB28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.mp4 30.1 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 30.1 MB35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 29.8 MB27. Advanced Statistical Methods - Linear regression/15. What is the OLS.mp4 29.7 MB48. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 29.3 MB48. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 29.2 MB42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 29.1 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 29.0 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4 28.9 MB10. Statistics - Descriptive Statistics/27. Covariance.mp4 28.8 MB10. Statistics - Descriptive Statistics/27. Covariance.vtt 28.8 MB31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 28.6 MB29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 28.4 MB13. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4 28.1 MB33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 28.0 MB18. Python - Variables and Data Types/1. Variables.mp4 27.9 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 27.6 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 27.2 MB43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 27.1 MB10. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 27.1 MB44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 27.0 MB48. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 26.9 MB50. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 26.7 MB45. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 26.5 MB39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 26.4 MB39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.vtt 26.4 MB35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 26.3 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 26.3 MB39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 26.3 MB23. Python - Sequences/7. Dictionaries.mp4 26.3 MB28. Advanced Statistical Methods - Multiple Linear Regression/20. Making Predictions with the Linear Regression.mp4 25.9 MB36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 25.6 MB39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 25.3 MB33. Part 5 Mathematics/14. Dot Product.mp4 25.2 MB22. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 25.0 MB35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 24.4 MB48. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4 24.3 MB29. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 24.2 MB12. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4 23.9 MB35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 23.8 MB43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 23.7 MB33. Part 5 Mathematics/8. What is a Tensor.mp4 23.6 MB12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 23.6 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 23.4 MB29. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 23.4 MB5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 23.1 MB22. Python - Python Functions/7. Built-in Functions in Python.mp4 23.1 MB23. Python - Sequences/1. Lists.mp4 23.1 MB23. Python - Sequences/3. Using Methods.mp4 23.0 MB28. Advanced Statistical Methods - Multiple Linear Regression/7. OLS Assumptions.mp4 22.9 MB40. Deep Learning - Initialization/1. What is Initialization.mp4 22.8 MB48. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 22.7 MB28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp4 22.6 MB31. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4 22.3 MB39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4 21.7 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp4 21.6 MB36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4 21.6 MB37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4 21.3 MB48. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp4 21.2 MB45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 21.1 MB13. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp4 20.9 MB25. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 20.9 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4 20.4 MB10. Statistics - Descriptive Statistics/19. Skewness.mp4 20.3 MB19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 19.8 MB43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4 19.8 MB42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4 19.5 MB25. Python - Advanced Python Tools/5. What is the Standard Library.mp4 18.9 MB35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 18.8 MB43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4 18.7 MB37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4 18.3 MB26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 18.2 MB40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 18.0 MB29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17.9 MB18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17.9 MB24. Python - Iterations/8. How to Iterate over Dictionaries.mp4 17.8 MB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp4 17.6 MB23. Python - Sequences/6. Tuples.mp4 17.5 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4 17.2 MB28. Advanced Statistical Methods - Multiple Linear Regression/6. Test for Significance of the Model (F-Test).mp4 17.2 MB24. Python - Iterations/6. Conditional Statements and Loops.mp4 16.9 MB22. Python - Python Functions/5. Conditional Statements and Functions.mp4 16.4 MB12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 16.2 MB24. Python - Iterations/3. While Loops and Incrementing.mp4 16.2 MB22. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 15.5 MB27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp4 15.5 MB30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 15.3 MB37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4 15.3 MB40. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 15.0 MB48. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4 14.7 MB17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 14.5 MB10. Statistics - Descriptive Statistics/11. The Histogram.mp4 14.4 MB48. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4 14.4 MB21. Python - Conditional Statements/1. The IF Statement.mp4 14.3 MB21. Python - Conditional Statements/3. The ELSE Statement.mp4 14.3 MB43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4 13.5 MB28. Advanced Statistical Methods - Multiple Linear Regression/9. A1 Linearity.mp4 13.2 MB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4 13.1 MB27. Advanced Statistical Methods - Linear regression/10. Using Seaborn for Graphs.mp4 12.8 MB44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 12.8 MB42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 12.4 MB24. Python - Iterations/1. For Loops.mp4 12.4 MB24. Python - Iterations/4. Lists with the range() Function.mp4 12.0 MB21. Python - Conditional Statements/5. A Note on Boolean Values.vtt 11.8 MB21. Python - Conditional Statements/5. A Note on Boolean Values.mp4 11.8 MB44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp4 11.8 MB33. Part 5 Mathematics/12. Errors when Adding Matrices.mp4 11.7 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4 11.6 MB20. Python - Other Python Operators/1. Comparison Operators.mp4 10.7 MB31. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4 10.4 MB24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4 9.9 MB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4 9.5 MB25. Python - Advanced Python Tools/3. Modules and Packages.mp4 8.9 MB22. Python - Python Functions/4. How to Use a Function within a Function.mp4 8.5 MB22. Python - Python Functions/1. Defining a Function in Python.mp4 8.1 MB22. Python - Python Functions/6. Functions Containing a Few Arguments.mp4 7.9 MB
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Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 4.9 kB15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).vtt 4.9 kB35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.vtt 4.9 kB29. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.vtt 4.9 kB15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).vtt 4.8 kB8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.vtt 4.8 kB33. Part 5 Mathematics/13. Transpose of a Matrix.vtt 4.8 kB44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.vtt 4.8 kB35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.vtt 4.7 kB45. Deep Learning - Conclusion/1. Summary on What You've Learned.vtt 4.7 kB37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.vtt 4.7 kB28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.vtt 4.7 kB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.vtt 4.7 kB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.vtt 4.7 kB35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.vtt 4.7 kB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt 4.7 kB45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.vtt 4.7 kB43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.vtt 4.6 kB1. Part 1 Introduction/2. What Does the Course Cover.vtt 4.6 kB15. Statistics - Hypothesis Testing/10. p-value.vtt 4.6 kB2. 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Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).vtt 4.0 kB48. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.vtt 4.0 kB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.vtt 4.0 kB36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).vtt 4.0 kB28. Advanced Statistical Methods - Multiple Linear Regression/20. Making Predictions with the Linear Regression.vtt 4.0 kB10. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.vtt 3.9 kB48. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.vtt 3.9 kB33. Part 5 Mathematics/1. What is a matrix.vtt 3.9 kB35. Deep Learning - Introduction to Neural Networks/3. Training the Model.vtt 3.9 kB22. Python - Python Functions/2. 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Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.vtt 3.7 kB33. Part 5 Mathematics/5. Linear Algebra and Geometry.vtt 3.6 kB30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.vtt 3.6 kB48. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.vtt 3.6 kB33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.vtt 3.6 kB23. Python - Sequences/3. Using Methods.vtt 3.6 kB12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.vtt 3.5 kB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.vtt 3.5 kB35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).vtt 3.5 kB42. Deep Learning - Preprocessing/1. Preprocessing Introduction.vtt 3.5 kB38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.vtt 3.5 kB24. Python - Iterations/8. How to Iterate over Dictionaries.vtt 3.4 kB27. Advanced Statistical Methods - Linear regression/15. What is the OLS.vtt 3.4 kB45. Deep Learning - Conclusion/5. An Overview of RNNs.vtt 3.4 kB47. Case Study - What's Next in the Course/2. The Business Task.vtt 3.4 kB33. Part 5 Mathematics/3. Scalars and Vectors.vtt 3.4 kB12. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.vtt 3.4 kB17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt 3.3 kB40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt 3.3 kB40. Deep Learning - Initialization/2. Types of Simple Initializations.vtt 3.3 kB48. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.vtt 3.3 kB10. Statistics - Descriptive Statistics/19. Skewness.vtt 3.3 kB18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.vtt 3.2 kB33. Part 5 Mathematics/8. What is a Tensor.vtt 3.2 kB43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.vtt 3.2 kB25. Python - Advanced Python Tools/5. What is the Standard Library.vtt 3.2 kB24. Python - Iterations/6. Conditional Statements and Loops.vtt 3.2 kB5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.vtt 3.2 kB49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.vtt 3.2 kB21. Python - Conditional Statements/1. The IF Statement.vtt 3.2 kB43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.vtt 3.2 kB40. Deep Learning - Initialization/1. What is Initialization.vtt 3.2 kB43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.vtt 3.1 kB22. Python - Python Functions/5. Conditional Statements and Functions.vtt 3.1 kB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.vtt 3.1 kB37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.vtt 3.1 kB23. Python - Sequences/6. Tuples.vtt 3.0 kB28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.vtt 3.0 kB41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).vtt 3.0 kB29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.vtt 3.0 kB30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.vtt 3.0 kB13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.vtt 2.9 kB37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.vtt 2.9 kB
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Part 5 Mathematics/13.1 Transpose of a Matrix Python Notebook.html 167 Bytes48. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167 Bytes43. Deep Learning - Classifying on the MNIST Dataset/11.6 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html 165 Bytes43. Deep Learning - Classifying on the MNIST Dataset/11.9 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html 165 Bytes37. Deep Learning - TensorFlow Introduction/9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html 162 Bytes37. Deep Learning - TensorFlow Introduction/9.5 Basic NN Example with TensorFlow Exercise 2.3 Solution.html 162 Bytes37. Deep Learning - TensorFlow Introduction/9.6 Basic NN Example with TensorFlow Exercise 2.2 Solution.html 162 Bytes37. Deep Learning - TensorFlow Introduction/9.8 Basic NN Example with TensorFlow Exercise 2.4 Solution.html 162 Bytes43. Deep Learning - Classifying on the MNIST Dataset/11.1 TensorFlow MNIST 'Time' Solution.html 162 Bytes43. Deep Learning - Classifying on the MNIST Dataset/11.3 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html 162 Bytes43. Deep Learning - Classifying on the MNIST Dataset/11.7 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html 162 Bytes10. Statistics - Descriptive Statistics/12. The Histogram.html 161 Bytes10. Statistics - Descriptive Statistics/15. Cross Tables and Scatter Plots.html 161 Bytes10. Statistics - Descriptive Statistics/2. Types of Data.html 161 Bytes10. Statistics - Descriptive Statistics/20. Skewness.html 161 Bytes10. Statistics - Descriptive Statistics/25. Standard Deviation.html 161 Bytes10. Statistics - Descriptive Statistics/28. Covariance.html 161 Bytes10. Statistics - Descriptive Statistics/31. Correlation.html 161 Bytes10. Statistics - Descriptive Statistics/4. Levels of Measurement.html 161 Bytes10. Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html 161 Bytes10. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html 161 Bytes12. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html 161 Bytes13. Statistics - Inferential Statistics Confidence Intervals/11. Margin of Error.html 161 Bytes13. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html 161 Bytes13. Statistics - Inferential Statistics Confidence Intervals/7. Student's T Distribution.html 161 Bytes15. Statistics - Hypothesis Testing/11. p-value.html 161 Bytes15. Statistics - Hypothesis Testing/19. Test for the mean. Independent samples (Part 2).html 161 Bytes15. Statistics - Hypothesis Testing/3. Null vs Alternative Hypothesis.html 161 Bytes15. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html 161 Bytes15. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html 161 Bytes17. Part 3 Introduction to Python/10. Jupyter's Interface.html 161 Bytes17. Part 3 Introduction to Python/2. Introduction to Programming.html 161 Bytes17. Part 3 Introduction to Python/4. Why Python.html 161 Bytes17. Part 3 Introduction to Python/6. Why Jupyter.html 161 Bytes18. Python - Variables and Data Types/2. Variables.html 161 Bytes18. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html 161 Bytes18. Python - Variables and Data Types/6. Python Strings.html 161 Bytes19. Python - Basic Python Syntax/11. Indexing Elements.html 161 Bytes19. Python - Basic Python Syntax/13. Structuring with Indentation.html 161 Bytes19. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html 161 Bytes19. Python - Basic Python Syntax/4. The Double Equality Sign.html 161 Bytes19. Python - Basic Python Syntax/6. How to Reassign Values.html 161 Bytes19. Python - Basic Python Syntax/8. Add Comments.html 161 Bytes2. The Field of Data Science - The Various Data Science Disciplines/10. A Breakdown of our Data Science Infographic.html 161 Bytes2. The Field of Data Science - The Various Data Science Disciplines/2. Data Science and Business Buzzwords Why are there so many.html 161 Bytes2. The Field of Data Science - The Various Data Science Disciplines/4. What is the difference between Analysis and Analytics.html 161 Bytes2. The Field of Data Science - The Various Data Science Disciplines/6. Business Analytics, Data Analytics, and Data Science An Introduction.html 161 Bytes2. The Field of Data Science - The Various Data Science Disciplines/8. Continuing with BI, ML, and AI.html 161 Bytes20. Python - Other Python Operators/2. Comparison Operators.html 161 Bytes20. Python - Other Python Operators/4. Logical and Identity Operators.html 161 Bytes21. Python - Conditional Statements/2. The IF Statement.html 161 Bytes21. Python - Conditional Statements/6. A Note on Boolean Values.html 161 Bytes22. Python - Python Functions/8. Python Functions.html 161 Bytes23. Python - Sequences/2. Lists.html 161 Bytes23. Python - Sequences/4. Using Methods.html 161 Bytes23. Python - Sequences/8. Dictionaries.html 161 Bytes24. Python - Iterations/2. For Loops.html 161 Bytes24. Python - Iterations/5. Lists with the range() Function.html 161 Bytes25. Python - Advanced Python Tools/2. Object Oriented Programming.html 161 Bytes25. Python - Advanced Python Tools/4. Modules and Packages.html 161 Bytes25. Python - Advanced Python Tools/6. What is the Standard Library.html 161 Bytes25. Python - Advanced Python Tools/8. Importing Modules in Python.html 161 Bytes26. Part 4 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html 161 Bytes27. Advanced Statistical Methods - Linear regression/12. How to Interpret the Regression Table.html 161 Bytes27. Advanced Statistical Methods - Linear regression/14. Decomposition of Variability.html 161 Bytes27. Advanced Statistical Methods - Linear regression/16. What is the OLS.html 161 Bytes27. Advanced Statistical Methods - Linear regression/18. R-Squared.html 161 Bytes27. Advanced Statistical Methods - Linear regression/2. The Linear Regression Model.html 161 Bytes27. Advanced Statistical Methods - Linear regression/4. Correlation vs Regression.html 161 Bytes27. Advanced Statistical Methods - Linear regression/6. Geometrical Representation of the Linear Regression Model.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/10. A1 Linearity.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/12. A2 No Endogeneity.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/15. A4 No autocorrelation.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/17. A5 No Multicollinearity.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/2. Multiple Linear Regression.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/4. Adjusted R-Squared.html 161 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/8. OLS Assumptions.html 161 Bytes3. The Field of Data Science - Connecting the Data Science Disciplines/2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html 161 Bytes33. Part 5 Mathematics/11. Addition and Subtraction of Matrices.html 161 Bytes33. Part 5 Mathematics/2. What is a Matrix.html 161 Bytes33. Part 5 Mathematics/4. Scalars and Vectors.html 161 Bytes33. Part 5 Mathematics/6. Linear Algebra and Geometry.html 161 Bytes33. Part 5 Mathematics/9. What is a Tensor.html 161 Bytes34. Part 6 Deep Learning/2. What is Machine Learning.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/4. Training the Model.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html 161 Bytes35. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html 161 Bytes4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason behind these Disciplines.html 161 Bytes46. Software Integration/10. Software Integration - Explained.html 161 Bytes46. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html 161 Bytes46. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html 161 Bytes46. Software Integration/6. Taking a Closer Look at APIs.html 161 Bytes46. Software Integration/8. Communication between Software Products through Text Files.html 161 Bytes47. Case Study - What's Next in the Course/4. Introducing the Data Set.html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html 161 Bytes5. The Field of Data Science - Popular Data Science Techniques/5. 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Deep Learning - Business Case Example/8.1 TensorFlow Business Case Optimization.html 134 Bytes44. Deep Learning - Business Case Example/9.1 TensorFlow Business Case Interpretation.html 134 Bytes50. Case Study - Loading the 'absenteeism_module'/1.1 5 Files Needed to Deploy the Model.html 134 Bytes48. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 Bytes
[FreeCourseLab.com].url 126 Bytes48. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 117 Bytes48. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html 116 Bytes48. Case Study - Preprocessing the 'Absenteeism_data'/9. SOLUTION - Dropping a Column from a DataFrame in Python.html 113 Bytes29. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes29. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html 87 Bytes29. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html 87 Bytes29. Advanced Statistical Methods - Logistic Regression/5. Building a Logistic Regression - Exercise.html 87 Bytes29. Advanced Statistical Methods - Logistic Regression/8. Understanding Logistic Regression Tables - Exercise.html 87 Bytes31. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes31. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes31. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html 87 Bytes31. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data.html 87 Bytes31. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html 87 Bytes10. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/13. Histogram Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/21. Skewness Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/29. Covariance Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html 81 Bytes10. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html 81 Bytes11. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html 81 Bytes12. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html 81 Bytes13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html 81 Bytes13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html 81 Bytes13. Statistics - Inferential Statistics Confidence Intervals/4. Confidence Intervals; Population Variance Known; z-score; Exercise.html 81 Bytes13. Statistics - Inferential Statistics Confidence Intervals/9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html 81 Bytes14. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html 81 Bytes15. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes15. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html 81 Bytes15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 1). Exercise.html 81 Bytes15. Statistics - Hypothesis Testing/20. Test for the mean. Independent samples (Part 2) Exercise.html 81 Bytes15. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html 81 Bytes16. Statistics - Practical Example Hypothesis Testing/2. Practical Example Hypothesis Testing Exercise.html 81 Bytes27. Advanced Statistical Methods - Linear regression/9. First Regression in Python Exercise.html 76 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/19. Dealing with Categorical Data - Dummy Variables.html 76 Bytes28. Advanced Statistical Methods - Multiple Linear Regression/5. Multiple Linear Regression Exercise.html 76 Bytes
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