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11. Probability - Bayesian Inference/12. A Practical Example of Bayesian Inference.mp4 146.0 MB12. Probability - Distributions/15. A Practical Example of Probability Distributions.mp4 145.0 MB16. Statistics - Practical Example Descriptive Statistics/01. Practical Example Descriptive Statistics.mp4 136.9 MB05. The Field of Data Science - Popular Data Science Techniques/01. Techniques for Working with Traditional Data.mp4 112.4 MB42. Part 6 Mathematics/11. Why is Linear Algebra Useful.mp4 92.8 MB35. Advanced Statistical Methods - Practical Example Linear Regression/01. Practical Example Linear Regression (Part 1).mp4 88.9 MB03. The Field of Data Science - Connecting the Data Science Disciplines/01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 87.6 MB06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.mp4 86.4 MB10. Probability - Combinatorics/11. A Practical Example of Combinatorics.mp4 84.6 MB05. The Field of Data Science - Popular Data Science Techniques/07. Techniques for Working with Traditional Methods.mp4 79.7 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/04. Business Case Preprocessing.mp4 78.0 MB53. Deep Learning - Business Case Example/04. Business Case Preprocessing the Data.mp4 77.4 MB60. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 73.1 MB05. The Field of Data Science - Popular Data Science Techniques/10. Types of Machine Learning.mp4 72.8 MB19. Statistics - Practical Example Inferential Statistics/01. Practical Example Inferential Statistics.mp4 72.4 MB05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.mp4 65.1 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/01. Business Case Getting Acquainted with the Dataset.mp4 63.2 MB58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.mp4 63.1 MB08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.mp4 61.7 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/06. Creating a Data Provider.mp4 59.0 MB60. Case Study - Preprocessing the 'Absenteeism_data'/03. Checking the Content of the Data Set.mp4 56.6 MB05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.mp4 55.5 MB18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.mp4 54.7 MB60. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 53.8 MB53. Deep Learning - Business Case Example/01. Business Case Exploring the Dataset and Identifying Predictors.mp4 53.8 MB35. Advanced Statistical Methods - Practical Example Linear Regression/08. Practical Example Linear Regression (Part 5).mp4 52.9 MB05. The Field of Data Science - Popular Data Science Techniques/09. Machine Learning (ML) Techniques.mp4 51.8 MB02. The Field of Data Science - The Various Data Science Disciplines/04. Continuing with BI, ML, and AI.mp4 49.9 MB04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.mp4 49.0 MB21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.mp4 48.1 MB09. Part 2 Probability/02. Computing Expected Values.mp4 47.9 MB02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.mp4 47.6 MB62. Case Study - Loading the 'absenteeism_module'/03. Deploying the 'absenteeism_module' - Part II.mp4 47.3 MB18. Statistics - Inferential Statistics Confidence Intervals/09. Confidence intervals. Two means. Dependent samples.mp4 47.2 MB53. Deep Learning - Business Case Example/09. Business Case Setting an Early Stopping Mechanism.mp4 45.9 MB64. Appendix - Additional Python Tools/05. List Comprehensions.mp4 45.3 MB15. Statistics - Descriptive Statistics/01. Types of Data.mp4 45.3 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/07. Business Case Model Outline.mp4 44.6 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.mp4 44.1 MB60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.mp4 43.2 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.mp4 43.1 MB13. Probability - Probability in Other Fields/01. Probability in Finance.mp4 42.3 MB63. Case Study - Analyzing the Predicted Outputs in Tableau/04. Analyzing Reasons vs Probability in Tableau.mp4 42.2 MB60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 42.1 MB07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.mp4 42.0 MB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/04. Basic NN Example (Part 4).mp4 41.9 MB35. Advanced Statistical Methods - Practical Example Linear Regression/06. Practical Example Linear Regression (Part 4).mp4 41.3 MB20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.mp4 40.6 MB63. Case Study - Analyzing the Predicted Outputs in Tableau/02. Analyzing Age vs Probability in Tableau.mp4 40.6 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.mp4 40.0 MB38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 39.3 MB09. Part 2 Probability/03. Frequency.mp4 39.2 MB65. Appendix - pandas Fundamentals/11. Data Selection in pandas DataFrames.mp4 39.1 MB20. Statistics - Hypothesis Testing/05. Test for the Mean. Population Variance Known.mp4 38.7 MB05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.mp4 38.5 MB40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.mp4 38.5 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.mp4 37.8 MB37. Advanced Statistical Methods - Cluster Analysis/02. Some Examples of Clusters.mp4 37.6 MB12. Probability - Distributions/02. Types of Probability Distributions.mp4 37.3 MB34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 37.3 MB14. Part 3 Statistics/01. Population and Sample.mp4 36.8 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.mp4 36.4 MB42. Part 6 Mathematics/10. Dot Product of Matrices.mp4 36.0 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/02. Adjusted R-Squared.mp4 35.9 MB38. Advanced Statistical Methods - K-Means Clustering/02. A Simple Example of Clustering.mp4 35.8 MB38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 35.7 MB60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 35.5 MB20. Statistics - Hypothesis Testing/07. p-value.mp4 35.4 MB40. ChatGPT for Data Science/10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4 35.3 MB40. ChatGPT for Data Science/19. Using ChatGPT for ethical considerations.mp4 35.2 MB40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4 34.7 MB64. Appendix - Additional Python Tools/04. Triple Nested For Loops.mp4 34.6 MB20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.mp4 34.4 MB52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.mp4 34.3 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.mp4 34.0 MB28. Python - Sequences/05. Dictionaries.mp4 34.0 MB65. Appendix - pandas Fundamentals/12. pandas DataFrames - Indexing with .iloc[].mp4 33.8 MB15. Statistics - Descriptive Statistics/02. Levels of Measurement.mp4 33.8 MB20. Statistics - Hypothesis Testing/01. Null vs Alternative Hypothesis.mp4 33.5 MB35. Advanced Statistical Methods - Practical Example Linear Regression/02. Practical Example Linear Regression (Part 2).mp4 33.4 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/08. MNIST Learning.mp4 33.4 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 33.4 MB13. Probability - Probability in Other Fields/02. Probability in Statistics.mp4 33.1 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 33.1 MB52. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4 32.5 MB12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.mp4 32.1 MB02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.mp4 32.0 MB28. Python - Sequences/02. Using Methods.mp4 31.8 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/05. First Regression in Python.mp4 31.0 MB53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.mp4 30.8 MB09. Part 2 Probability/01. The Basic Probability Formula.mp4 30.8 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.mp4 30.1 MB40. ChatGPT for Data Science/04. Data Preprocessing with ChatGPT.mp4 30.1 MB18. Statistics - Inferential Statistics Confidence Intervals/01. What are Confidence Intervals.mp4 30.0 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 29.9 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/10. Machine Learning with Naïve Bayes (First Attempt).mp4 29.5 MB38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 29.4 MB05. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Machine Learning (ML).mp4 29.1 MB17. Statistics - Inferential Statistics Fundamentals/08. Estimators and Estimates.mp4 29.0 MB60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 29.0 MB15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.mp4 28.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/03. Simple Linear Regression with sklearn.mp4 28.8 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/08. A3 Normality and Homoscedasticity.mp4 28.7 MB60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 28.7 MB46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.mp4 28.7 MB05. The Field of Data Science - Popular Data Science Techniques/11. Evolution and Latest Trends of Machine Learning (ML).mp4 28.7 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/03. The Importance of Working with a Balanced Dataset.mp4 28.6 MB40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp4 28.5 MB60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 28.3 MB46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.mp4 28.3 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.mp4 28.3 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.mp4 28.2 MB38. Advanced Statistical Methods - K-Means Clustering/06. How to Choose the Number of Clusters.mp4 28.2 MB40. ChatGPT for Data Science/01. Traditional data science methods and the role of ChatGPT.mp4 27.4 MB46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.mp4 27.2 MB09. Part 2 Probability/04. Events and Their Complements.mp4 27.1 MB64. Appendix - Additional Python Tools/01. Using the .format() Method.mp4 26.9 MB65. Appendix - pandas Fundamentals/10. pandas DataFrames - Common Attributes.mp4 26.9 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 26.8 MB65. Appendix - pandas Fundamentals/01. Introduction to pandas Series.mp4 26.2 MB36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 26.1 MB05. The Field of Data Science - Popular Data Science Techniques/06. Real Life Examples of Business Intelligence (BI).mp4 25.8 MB02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.mp4 25.7 MB58. Software Integration/03. Taking a Closer Look at APIs.mp4 25.7 MB15. Statistics - Descriptive Statistics/11. Mean, median and mode.mp4 25.7 MB34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 25.7 MB20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).mp4 25.6 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/06. Calculating the Accuracy of the Model.mp4 25.6 MB65. Appendix - pandas Fundamentals/06. Using .unique() and .nunique().mp4 25.5 MB59. Case Study - What's Next in the Course/03. Introducing the Data Set.mp4 25.4 MB11. Probability - Bayesian Inference/04. Union of Sets.mp4 25.4 MB12. Probability - Distributions/07. Discrete Distributions The Poisson Distribution.mp4 25.1 MB36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.mp4 24.9 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.mp4 24.8 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.mp4 24.8 MB10. Probability - Combinatorics/06. Solving Combinations.mp4 24.8 MB44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.mp4 24.7 MB15. Statistics - Descriptive Statistics/15. Variance.mp4 24.7 MB17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.mp4 24.3 MB18. Statistics - Inferential Statistics Confidence Intervals/08. Margin of Error.mp4 24.2 MB28. Python - Sequences/01. Lists.mp4 24.2 MB52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 24.0 MB64. Appendix - Additional Python Tools/06. Anonymous (Lambda) Functions.mp4 23.9 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. Dealing with Categorical Data - Dummy Variables.mp4 23.7 MB52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4 23.7 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.mp4 23.6 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/10. What is the OLS.mp4 23.6 MB53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.mp4 23.4 MB34. Advanced Statistical Methods - Linear Regression with sklearn/04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 23.4 MB42. Part 6 Mathematics/06. Addition and Subtraction of Matrices.mp4 23.2 MB52. Deep Learning - Classifying on the MNIST Dataset/08. MNIST Outline the Model.mp4 23.1 MB36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.mp4 22.9 MB40. ChatGPT for Data Science/06. Analyzing a client database with ChatGPT in Python.mp4 22.7 MB40. ChatGPT for Data Science/09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4 22.6 MB36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 22.6 MB11. Probability - Bayesian Inference/11. Bayes' Law.mp4 22.4 MB12. Probability - Distributions/08. Characteristics of Continuous Distributions.mp4 22.3 MB65. Appendix - pandas Fundamentals/05. Parameters and Arguments in pandas.mp4 22.2 MB12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.mp4 22.1 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.mp4 21.8 MB65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].mp4 21.7 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4 21.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 21.5 MB34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 21.4 MB34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 21.4 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/07. Backpropagation.mp4 21.3 MB10. Probability - Combinatorics/08. Solving Combinations with Separate Sample Spaces.mp4 21.3 MB36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 21.2 MB11. Probability - Bayesian Inference/10. The Multiplication Law.mp4 21.2 MB29. Python - Iterations/02. While Loops and Incrementing.mp4 21.2 MB15. Statistics - Descriptive Statistics/17. Standard Deviation and Coefficient of Variation.mp4 21.1 MB11. Probability - Bayesian Inference/07. The Conditional Probability Formula.mp4 21.0 MB12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.mp4 21.0 MB23. Python - Variables and Data Types/03. Python Strings.mp4 20.7 MB20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.mp4 20.7 MB15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.mp4 20.7 MB59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 20.6 MB62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.mp4 20.6 MB60. Case Study - Preprocessing the 'Absenteeism_data'/02. Importing the Absenteeism Data in Python.mp4 20.5 MB58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.mp4 20.5 MB12. Probability - Distributions/01. Fundamentals of Probability Distributions.mp4 20.4 MB15. Statistics - Descriptive Statistics/21. Correlation Coefficient.mp4 20.3 MB28. Python - Sequences/03. List Slicing.mp4 20.1 MB60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 20.1 MB25. Python - Other Python Operators/02. Logical and Identity Operators.mp4 19.9 MB42. Part 6 Mathematics/04. Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.9 MB22. Part 4 Introduction to Python/06. Prerequisites for Coding in the Jupyter Notebooks.mp4 19.9 MB18. Statistics - Inferential Statistics Confidence Intervals/04. Confidence Interval Clarifications.mp4 19.9 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp4 19.8 MB22. Part 4 Introduction to Python/04. Installing Python and Jupyter.mp4 19.7 MB36. Advanced Statistical Methods - Logistic Regression/06. An Invaluable Coding Tip.mp4 19.7 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.mp4 19.6 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/09. Business Case Interpretation.mp4 19.5 MB39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.mp4 19.4 MB29. Python - Iterations/06. How to Iterate over Dictionaries.mp4 19.3 MB05. The Field of Data Science - Popular Data Science Techniques/02. Real Life Examples of Traditional Data.mp4 19.3 MB15. Statistics - Descriptive Statistics/19. Covariance.mp4 19.3 MB39. Advanced Statistical Methods - Other Types of Clustering/02. Dendrogram.mp4 19.2 MB10. Probability - Combinatorics/05. Solving Variations without Repetition.mp4 19.1 MB28. Python - Sequences/04. Tuples.mp4 19.1 MB60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.mp4 18.9 MB40. ChatGPT for Data Science/17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp4 18.7 MB65. Appendix - pandas Fundamentals/09. Introduction to pandas DataFrames - Part II.mp4 18.7 MB15. Statistics - Descriptive Statistics/05. Numerical Variables - Frequency Distribution Table.mp4 18.6 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 18.5 MB11. Probability - Bayesian Inference/01. Sets and Events.mp4 18.5 MB58. Software Integration/04. Communication between Software Products through Text Files.mp4 18.4 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 18.4 MB10. Probability - Combinatorics/02. Permutations and How to Use Them.mp4 18.4 MB60. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 18.2 MB29. Python - Iterations/04. Conditional Statements and Loops.mp4 18.2 MB40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4 18.1 MB17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.mp4 18.0 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/09. Basic NN Example with TF Model Output.mp4 17.9 MB34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.mp4 17.7 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.mp4 17.7 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 17.7 MB44. Deep Learning - Introduction to Neural Networks/12. Optimization Algorithm n-Parameter Gradient Descent.mp4 17.7 MB46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.mp4 17.6 MB35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).mp4 17.5 MB44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.mp4 17.4 MB63. Case Study - Analyzing the Predicted Outputs in Tableau/06. Analyzing Transportation Expense vs Probability in Tableau.mp4 17.3 MB10. Probability - Combinatorics/09. Combinatorics in Real-Life The Lottery.mp4 17.2 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.mp4 17.1 MB12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.mp4 17.0 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.mp4 17.0 MB54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.mp4 16.9 MB29. Python - Iterations/03. Lists with the range() Function.mp4 16.8 MB58. Software Integration/05. Software Integration - Explained.mp4 16.8 MB12. Probability - Distributions/13. Continuous Distributions The Exponential Distribution.mp4 16.8 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/03. Tokenization and Vectorization.mp4 16.6 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/05. MNIST Loss and Optimization Algorithm.mp4 16.6 MB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/03. Basic NN Example (Part 3).mp4 16.4 MB02. The Field of Data Science - The Various Data Science Disciplines/01. Data Science and Business Buzzwords Why are there so Many.mp4 16.3 MB
66. Bonus Lecture/assets/01. 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 16.3 MB42. Part 6 Mathematics/05. What is a Tensor.mp4 16.3 MB20. Statistics - Hypothesis Testing/12. Test for the mean. Independent Samples (Part 1).mp4 16.2 MB46. Deep Learning - TensorFlow 2.0 Introduction/03. TensorFlow 1 vs TensorFlow 2.mp4 16.0 MB20. Statistics - Hypothesis Testing/04. Type I Error and Type II Error.mp4 16.0 MB46. Deep Learning - TensorFlow 2.0 Introduction/02. TensorFlow Outline and Comparison with Other Libraries.mp4 16.0 MB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/02. Basic NN Example (Part 2).mp4 16.0 MB65. Appendix - pandas Fundamentals/07. Using .sort_values().mp4 16.0 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/06. Fitting the Model and Assessing its Accuracy.mp4 16.0 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 15.9 MB40. ChatGPT for Data Science/07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp4 15.9 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/04. Standardizing the Data.mp4 15.9 MB12. Probability - Distributions/05. Discrete Distributions The Bernoulli Distribution.mp4 15.9 MB40. ChatGPT for Data Science/13. Marvels comic book database Intro to Regular Expressions (RegEx).mp4 15.7 MB11. Probability - Bayesian Inference/06. Dependence and Independence of Sets.mp4 15.6 MB22. Part 4 Introduction to Python/01. Introduction to Programming.mp4 15.6 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/06. Loading the Dataset and Preprocessing.mp4 15.5 MB40. ChatGPT for Data Science/18. Ethical principles in data and AI utilization.mp4 15.4 MB18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent Samples (Part 2).mp4 15.3 MB02. The Field of Data Science - The Various Data Science Disciplines/03. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 15.3 MB36. Advanced Statistical Methods - Logistic Regression/07. Understanding Logistic Regression Tables.mp4 15.3 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/05. Overcome Imbalanced Data in Machine Learning.mp4 15.3 MB37. Advanced Statistical Methods - Cluster Analysis/01. Introduction to Cluster Analysis.mp4 15.2 MB40. ChatGPT for Data Science/12. Hypothesis testing with ChatGPT.mp4 15.1 MB60. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 15.0 MB13. Probability - Probability in Other Fields/03. Probability in Data Science.mp4 14.9 MB26. Python - Conditional Statements/03. The ELIF Statement.mp4 14.9 MB42. Part 6 Mathematics/08. Transpose of a Matrix.mp4 14.9 MB11. Probability - Bayesian Inference/08. The Law of Total Probability.mp4 14.9 MB48. Deep Learning - Overfitting/02. Underfitting and Overfitting for Classification.mp4 14.7 MB10. Probability - Combinatorics/04. Solving Variations with Repetition.mp4 14.6 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp4 14.5 MB53. Deep Learning - Business Case Example/06. Business Case Load the Preprocessed Data.mp4 14.5 MB10. Probability - Combinatorics/07. Symmetry of Combinations.mp4 14.4 MB42. Part 6 Mathematics/03. Linear Algebra and Geometry.mp4 14.4 MB18. Statistics - Inferential Statistics Confidence Intervals/06. Confidence Intervals; Population Variance Unknown; T-score.mp4 14.4 MB18. Statistics - Inferential Statistics Confidence Intervals/05. Student's T Distribution.mp4 14.3 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/08. Basic NN Example with TF Loss Function and Gradient Descent.mp4 14.3 MB60. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 14.2 MB17. Statistics - Inferential Statistics Fundamentals/07. Standard error.mp4 14.2 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/01. The Linear Regression Model.mp4 14.1 MB54. Deep Learning - Conclusion/04. An overview of CNNs.mp4 14.0 MB15. Statistics - Descriptive Statistics/13. Skewness.mp4 14.0 MB65. Appendix - pandas Fundamentals/03. Working with Methods in Python - Part I.mp4 13.9 MB17. Statistics - Inferential Statistics Fundamentals/03. The Normal Distribution.mp4 13.7 MB44. Deep Learning - Introduction to Neural Networks/03. Types of Machine Learning.mp4 13.7 MB05. The Field of Data Science - Popular Data Science Techniques/04. Real Life Examples of Big Data.mp4 13.7 MB29. Python - Iterations/01. For Loops.mp4 13.6 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/01. Exploring the Problem with a Machine Learning Mindset.mp4 13.6 MB42. Part 6 Mathematics/09. Dot Product.mp4 13.5 MB64. Appendix - Additional Python Tools/02. Iterating Over Range Objects.mp4 13.2 MB65. Appendix - pandas Fundamentals/08. Introduction to pandas DataFrames - Part I.mp4 13.1 MB52. Deep Learning - Classifying on the MNIST Dataset/03. MNIST Importing the Relevant Packages and Loading the Data.mp4 12.8 MB22. Part 4 Introduction to Python/02. Why Python.mp4 12.8 MB64. Appendix - Additional Python Tools/03. Introduction to Nested For Loops.mp4 12.8 MB10. Probability - Combinatorics/10. A Recap of Combinatorics.mp4 12.7 MB51. Deep Learning - Preprocessing/03. Standardization.mp4 12.7 MB
40. ChatGPT for Data Science/assets/16. movies-metadata.zip 12.6 MB18. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.6 MB42. Part 6 Mathematics/01. What is a Matrix.mp4 12.5 MB43. Part 7 Deep Learning/01. What to Expect from this Part.mp4 12.3 MB36. Advanced Statistical Methods - Logistic Regression/09. What do the Odds Actually Mean.mp4 11.9 MB11. Probability - Bayesian Inference/02. Ways Sets Can Interact.mp4 11.9 MB59. Case Study - What's Next in the Course/02. The Business Task.mp4 11.8 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/03. MNIST Relevant Packages.mp4 11.8 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/11. R-Squared.mp4 11.7 MB02. The Field of Data Science - The Various Data Science Disciplines/02. What is the difference between Analysis and Analytics.mp4 11.7 MB12. Probability - Distributions/12. Continuous Distributions The Chi-Squared Distribution.mp4 11.7 MB38. Advanced Statistical Methods - K-Means Clustering/08. Pros and Cons of K-Means Clustering.mp4 11.7 MB11. Probability - Bayesian Inference/09. The Additive Rule.mp4 11.6 MB11. Probability - Bayesian Inference/03. Intersection of Sets.mp4 11.6 MB38. Advanced Statistical Methods - K-Means Clustering/09. To Standardize or not to Standardize.mp4 11.4 MB38. Advanced Statistical Methods - K-Means Clustering/01. K-Means Clustering.mp4 11.3 MB48. Deep Learning - Overfitting/01. What is Overfitting.mp4 11.3 MB01. Part 1 Introduction/01. A Practical Example What You Will Learn in This Course.mp4 11.3 MB52. Deep Learning - Classifying on the MNIST Dataset/09. MNIST Select the Loss and the Optimizer.mp4 11.2 MB11. Probability - Bayesian Inference/05. Mutually Exclusive Sets.mp4 11.1 MB10. Probability - Combinatorics/03. Simple Operations with Factorials.mp4 11.0 MB44. Deep Learning - Introduction to Neural Networks/01. Introduction to Neural Networks.mp4 11.0 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/01. Intro to the Case Study.mp4 10.9 MB38. Advanced Statistical Methods - K-Means Clustering/04. Clustering Categorical Data.mp4 10.9 MB12. Probability - Distributions/04. Discrete Distributions The Uniform Distribution.mp4 10.8 MB48. Deep Learning - Overfitting/06. Early Stopping or When to Stop Training.mp4 10.8 MB27. Python - Python Functions/07. Built-in Functions in Python.mp4 10.7 MB60. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4 10.5 MB27. Python - Python Functions/02. How to Create a Function with a Parameter.mp4 10.5 MB60. Case Study - Preprocessing the 'Absenteeism_data'/06. Using a Statistical Approach towards the Solution to the Exercise.mp4 10.4 MB30. Python - Advanced Python Tools/04. Importing Modules in Python.mp4 10.4 MB54. Deep Learning - Conclusion/01. Summary on What You've Learned.mp4 10.3 MB44. Deep Learning - Introduction to Neural Networks/10. Common Objective Functions Cross-Entropy Loss.mp4 10.3 MB37. Advanced Statistical Methods - Cluster Analysis/03. Difference between Classification and Clustering.mp4 10.1 MB15. Statistics - Descriptive Statistics/07. The Histogram.mp4 10.0 MB01. Part 1 Introduction/02. What Does the Course Cover.mp4 10.0 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/07. MNIST Batching and Early Stopping.mp4 9.9 MB12. Probability - Distributions/03. Characteristics of Discrete Distributions.mp4 9.9 MB48. Deep Learning - Overfitting/04. Training, Validation, and Test Datasets.mp4 9.9 MB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/01. Basic NN Example (Part 1).mp4 9.8 MB12. Probability - Distributions/11. Continuous Distributions The Students' T Distribution.mp4 9.7 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/07. A2 No Endogeneity.mp4 9.7 MB51. Deep Learning - Preprocessing/01. Preprocessing Introduction.mp4 9.7 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/02. What is a Deep Net.mp4 9.6 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/05. Actual Introduction to TensorFlow.mp4 9.5 MB39. Advanced Statistical Methods - Other Types of Clustering/01. Types of Clustering.mp4 9.4 MB65. Appendix - pandas Fundamentals/04. Working with Methods in Python - Part II.mp4 9.4 MB23. Python - Variables and Data Types/01. Variables.mp4 9.4 MB49. Deep Learning - Initialization/01. What is Initialization.mp4 9.3 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/06. Types of File Formats, supporting Tensors.mp4 9.3 MB46. Deep Learning - TensorFlow 2.0 Introduction/05. Types of File Formats Supporting TensorFlow.mp4 9.3 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/05. Activation Functions.mp4 9.3 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/09. Decomposition of Variability.mp4 9.2 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/06. Activation Functions Softmax Activation.mp4 9.2 MB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/03. Selecting the Inputs for the Logistic Regression.mp4 9.1 MB30. Python - Advanced Python Tools/01. Object Oriented Programming.mp4 9.1 MB
12. Probability - Distributions/assets/15. FIFA19-post.csv 9.1 MB12. Probability - Distributions/assets/15. FIFA19.csv 9.1 MB24. Python - Basic Python Syntax/01. Using Arithmetic Operators in Python.mp4 9.0 MB17. Statistics - Inferential Statistics Fundamentals/04. The Standard Normal Distribution.mp4 9.0 MB36. Advanced Statistical Methods - Logistic Regression/04. Building a Logistic Regression.mp4 9.0 MB51. Deep Learning - Preprocessing/05. Binary and One-Hot Encoding.mp4 9.0 MB42. Part 6 Mathematics/02. Scalars and Vectors.mp4 9.0 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.9 MB34. Advanced Statistical Methods - Linear Regression with sklearn/01. What is sklearn and How is it Different from Other Packages.mp4 8.9 MB48. Deep Learning - Overfitting/03. What is Validation.mp4 8.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/07. Multiple Linear Regression with sklearn.mp4 8.7 MB53. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp4 8.6 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/08. Backpropagation Picture.mp4 8.5 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp4 8.4 MB22. Part 4 Introduction to Python/03. Why Jupyter.mp4 8.4 MB44. Deep Learning - Introduction to Neural Networks/04. The Linear Model (Linear Algebraic Version).mp4 8.4 MB52. Deep Learning - Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp4 8.3 MB44. Deep Learning - Introduction to Neural Networks/05. The Linear Model with Multiple Inputs.mp4 8.3 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/09. A4 No Autocorrelation.mp4 8.3 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/01. Stochastic Gradient Descent.mp4 8.2 MB44. Deep Learning - Introduction to Neural Networks/07. Graphical Representation of Simple Neural Networks.mp4 8.2 MB44. Deep Learning - Introduction to Neural Networks/02. Training the Model.mp4 8.1 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A5 No Multicollinearity.mp4 8.0 MB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/29. Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 8.0 MB36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 7.8 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/07. Using Seaborn for Graphs.mp4 7.7 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/04. Test for Significance of the Model (F-Test).mp4 7.5 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/07. Adam (Adaptive Moment Estimation).mp4 7.5 MB54. Deep Learning - Conclusion/05. An Overview of RNNs.mp4 7.3 MB
02. The Field of Data Science - The Various Data Science Disciplines/assets/04. 365-DataScience.png 7.3 MB02. The Field of Data Science - The Various Data Science Disciplines/assets/07. 365-DataScience.png 7.3 MB18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent Samples (Part 3).mp4 7.2 MB26. Python - Conditional Statements/01. The IF Statement.mp4 7.0 MB23. Python - Variables and Data Types/02. Numbers and Boolean Values in Python.mp4 6.9 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/04. Imbalanced Data Sets.mp4 6.9 MB27. Python - Python Functions/03. Defining a Function in Python - Part II.mp4 6.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.mp4 6.8 MB48. Deep Learning - Overfitting/05. N-Fold Cross Validation.mp4 6.5 MB44. Deep Learning - Introduction to Neural Networks/08. What is the Objective Function.mp4 6.5 MB22. Part 4 Introduction to Python/05. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 6.4 MB27. Python - Python Functions/05. Conditional Statements and Functions.mp4 6.3 MB26. Python - Conditional Statements/02. The ELSE Statement.mp4 6.3 MB10. Probability - Combinatorics/01. Fundamentals of Combinatorics.mp4 6.2 MB36. Advanced Statistical Methods - Logistic Regression/01. Introduction to Logistic Regression.mp4 6.2 MB34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp4 6.1 MB60. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4 6.1 MB42. Part 6 Mathematics/07. Errors when Adding Matrices.mp4 6.1 MB49. Deep Learning - Initialization/02. Types of Simple Initializations.mp4 6.0 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/01. Multiple Linear Regression.mp4 6.0 MB44. Deep Learning - Introduction to Neural Networks/09. Common Objective Functions L2-norm Loss.mp4 5.7 MB49. Deep Learning - Initialization/03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.7 MB51. Deep Learning - Preprocessing/04. Preprocessing Categorical Data.mp4 5.7 MB40. ChatGPT for Data Science/03. How ChatGPT can boost your productivity.mp4 5.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/02. How are we Going to Approach this Section.mp4 5.6 MB37. Advanced Statistical Methods - Cluster Analysis/04. Math Prerequisites.mp4 5.5 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/05. OLS Assumptions.mp4 5.5 MB40. ChatGPT for Data Science/02. How to install ChatGPT.mp4 5.5 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/03. Momentum.mp4 5.4 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/01. What is a Layer.mp4 5.4 MB30. Python - Advanced Python Tools/03. What is the Standard Library.mp4 5.3 MB55. Appendix Deep Learning - TensorFlow 1 Introduction/02. How to Install TensorFlow 1.mp4 5.2 MB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/01. MNIST What is the MNIST Dataset.mp4 5.0 MB54. Deep Learning - Conclusion/02. What's Further out there in terms of Machine Learning.mp4 5.0 MB46. Deep Learning - TensorFlow 2.0 Introduction/04. A Note on TensorFlow 2 Syntax.mp4 4.9 MB52. Deep Learning - Classifying on the MNIST Dataset/01. MNIST The Dataset.mp4 4.8 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp4 4.6 MB29. Python - Iterations/05. Conditional Statements, Functions, and Loops.mp4 4.5 MB26. Python - Conditional Statements/04. A Note on Boolean Values.mp4 4.4 MB25. Python - Other Python Operators/01. Comparison Operators.mp4 4.4 MB57. Appendix Deep Learning - TensorFlow 1 Business Case/02. Business Case Outlining the Solution.mp4 4.4 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/02. Correlation vs Regression.mp4 4.0 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/02. Problems with Gradient Descent.mp4 3.8 MB31. Part 5 Advanced Statistical Methods in Python/01. Introduction to Regression Analysis.mp4 3.8 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/06. A1 Linearity.mp4 3.7 MB40. ChatGPT for Data Science/assets/13. Marvel-Comics.zip 3.7 MB38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4 3.7 MB51. Deep Learning - Preprocessing/02. Types of Basic Preprocessing.mp4 3.4 MB27. Python - Python Functions/04. How to Use a Function within a Function.mp4 3.4 MB27. Python - Python Functions/01. Defining a Function in Python.mp4 3.4 MB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/05. Learning Rate Schedules Visualized.mp4 3.3 MB17. Statistics - Inferential Statistics Fundamentals/01. Introduction.mp4 3.2 MB53. Deep Learning - Business Case Example/02. Business Case Outlining the Solution.mp4 3.2 MB27. Python - Python Functions/06. Functions Containing a Few Arguments.mp4 2.9 MB24. Python - Basic Python Syntax/07. Structuring with Indentation.mp4 2.9 MB24. Python - Basic Python Syntax/02. The Double Equality Sign.mp4 2.8 MB24. Python - Basic Python Syntax/04. Add Comments.mp4 2.5 MB24. Python - Basic Python Syntax/06. Indexing Elements.mp4 2.5 MB40. ChatGPT for Data Science/assets/16. ratings-small.csv 2.4 MB32. Advanced Statistical Methods - Linear Regression with StatsModels/03. Geometrical Representation of the Linear Regression Model.mp4 2.4 MB22. Part 4 Introduction to Python/assets/01. Introduction-to-Python-Course-Notes.pdf 2.3 MB23. Python - Variables and Data Types/assets/01. Introduction-to-Python-Course-Notes.pdf 2.3 MB30. Python - Advanced Python Tools/02. Modules and Packages.mp4 2.2 MB24. Python - Basic Python Syntax/03. How to Reassign Values.mp4 2.0 MB19. Statistics - Practical Example Inferential Statistics/assets/02. 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB19. Statistics - Practical Example Inferential Statistics/assets/01. 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.8 MB19. Statistics - Practical Example Inferential Statistics/assets/02. 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.8 MB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/assets/12. 365-User-Reviews-Naive-Bayes-Sentiment-Analysis.ipynb 1.8 MB24. Python - Basic Python Syntax/05. Understanding Line Continuation.mp4 1.3 MB20. Statistics - Hypothesis Testing/assets/07. Online-p-value-calculator.pdf 1.2 MB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/assets/02. Course-Notes-Section-6.pdf 958.9 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/assets/01. Course-Notes-Section-6.pdf 958.9 kB11. Probability - Bayesian Inference/assets/12. CDS-2017-2018-Hamilton.pdf 865.6 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/08. sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB53. Deep Learning - Business Case Example/assets/01. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/12. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/03. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/04. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/01. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/05. Audiobooks-data.csv 727.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/11. Audiobooks-data.csv 727.8 kB35. 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Course-notes-descriptive-statistics.pdf 493.8 kB12. Probability - Distributions/assets/01. Course-Notes-Probability-Distributions.pdf 475.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/06. sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 417.4 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/06. sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 406.8 kB11. Probability - Bayesian Inference/assets/01. Course-Notes-Bayesian-Inference.pdf 395.3 kB17. Statistics - Inferential Statistics Fundamentals/assets/02. Course-notes-inferential-statistics.pdf 391.5 kB17. Statistics - Inferential Statistics Fundamentals/assets/01. Course-notes-inferential-statistics.pdf 391.5 kB09. Part 2 Probability/assets/01. Course-Notes-Basic-Probability.pdf 380.0 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/05. sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 379.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/04. sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 359.9 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/05. sklearn-Dummies-and-VIF-Exercise.ipynb 352.9 kB12. Probability - Distributions/assets/08. Solving-Integrals.pdf 352.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/04. sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 351.8 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/02. sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 343.7 kB36. Advanced Statistical Methods - Logistic Regression/assets/01. Course-Notes-Logistic-Regression.pdf 343.2 kB36. Advanced Statistical Methods - Logistic Regression/assets/02. Course-Notes-Logistic-Regression.pdf 343.2 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/02. sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB02. The Field of Data Science - The Various Data Science Disciplines/assets/04. 365-DataScience-Diagram.pdf 330.8 kB02. The Field of Data Science - The Various Data Science Disciplines/assets/03. 365-DataScience-Diagram.pdf 330.8 kB13. Probability - Probability in Other Fields/assets/03. Probability-Cheat-Sheet.pdf 328.0 kB31. Part 5 Advanced Statistical Methods in Python/assets/01. Course-notes-regression-analysis.pdf 319.7 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/01. Course-notes-regression-analysis.pdf 319.7 kB01. Part 1 Introduction/assets/03. FAQ-The-Data-Science-Course.pdf 313.4 kB15. Statistics - Descriptive Statistics/assets/04. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB15. Statistics - Descriptive Statistics/assets/08. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB40. ChatGPT for Data Science/assets/10. Properties-analysis.ipynb 293.4 kB10. Probability - Combinatorics/assets/11. Additional-Exercises-Combinatorics-Solutions.pdf 251.6 kB10. Probability - Combinatorics/assets/01. Course-Notes-Combinatorics.pdf 231.5 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/06. 1.04.Real-life-example.csv 225.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/02. 1.04.Real-life-example.csv 225.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/01. 1.04.Real-life-example.csv 225.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/05. 1.04.Real-life-example.csv 225.1 kB35. Advanced Statistical Methods - Practical Example Linear Regression/assets/08. 1.04.Real-life-example.csv 225.1 kB37. Advanced Statistical Methods - Cluster Analysis/assets/02. 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Lending-company.csv 115.1 kB36. Advanced Statistical Methods - Logistic Regression/assets/16. Testing-the-Model-Solution.ipynb 113.8 kB13. Probability - Probability in Other Fields/assets/01. Probability-in-Finance-Homework.pdf 113.3 kB10. Probability - Combinatorics/assets/11. Additional-Exercises-Combinatorics.pdf 109.1 kB10. Probability - Combinatorics/assets/07. Symmetry-Explained.pdf 87.1 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/09. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/09. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 85.7 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/09. TensorFlow-Minimal-example-All-exercises.ipynb 85.6 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/08. TensorFlow-Minimal-example-complete-with-comments.ipynb 84.3 kB36. Advanced Statistical Methods - Logistic Regression/assets/13. Calculating-the-Accuracy-of-the-Model-Solution.ipynb 83.2 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/09. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 79.4 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/08. TensorFlow-Minimal-example-complete.ipynb 78.7 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/07. TensorFlow-Minimal-example-Part3.ipynb 78.4 kB40. ChatGPT for Data Science/assets/19. interactions.csv 75.0 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-3.c.Solution.ipynb 71.8 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-1-Solution.ipynb 70.7 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-5-Solution.ipynb 70.5 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-3.a.Solution.ipynb 69.5 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-3.b.Solution.ipynb 69.3 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-4-Solution.ipynb 68.1 kB62. Case Study - Loading the 'absenteeism_module'/assets/01. Absenteeism-Exercise-Integration.ipynb 63.8 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-6-Solution.ipynb 63.2 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-6.ipynb 63.2 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-Exercise-2-Solution.ipynb 62.9 kB40. ChatGPT for Data Science/assets/08. Furniture-store-data-analysis.ipynb 53.6 kB21. Statistics - Practical Example Hypothesis Testing/assets/01. 4.10.Hypothesis-testing-section-practical-example.xlsx 53.1 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 51.2 kB21. Statistics - Practical Example Hypothesis Testing/assets/02. 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 45.3 kB21. Statistics - Practical Example Hypothesis Testing/assets/02. 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 44.7 kB44. Deep Learning - Introduction to Neural Networks/assets/11. GD-function-example.xlsx 43.4 kB15. Statistics - Descriptive Statistics/assets/04. 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 42.1 kB15. Statistics - Descriptive Statistics/assets/10. 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 41.4 kB40. ChatGPT for Data Science/assets/06. orders.csv 38.6 kB15. Statistics - Descriptive Statistics/assets/13. 2.8.Skewness-lesson.xlsx 35.5 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/01. Absenteeism-data.csv 32.8 kB65. Appendix - pandas Fundamentals/assets/13. pandas-Fundamentals-Exercises.ipynb 31.7 kB65. Appendix - pandas Fundamentals/assets/01. pandas-Fundamentals-Exercises.ipynb 31.7 kB40. ChatGPT for Data Science/assets/19. posts.csv 31.5 kB15. Statistics - Descriptive Statistics/assets/03. 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31.5 kB11. Probability - Bayesian Inference/assets/12. Bayesian-Homework-Solutions.pdf 31.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/16. sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 30.5 kB15. Statistics - Descriptive Statistics/assets/20. 2.11.Covariance-exercise-solution.xlsx 30.2 kB40. ChatGPT for Data Science/assets/14. Marvel-Comics-Reg-Ex.ipynb 30.2 kB15. Statistics - Descriptive Statistics/assets/22. 2.12.Correlation-exercise-solution.xlsx 30.2 kB15. Statistics - Descriptive Statistics/assets/22. 2.12.Correlation-exercise.xlsx 30.0 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/assets/01. Absenteeism-preprocessed.csv 29.8 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/01. df-preprocessed.csv 29.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/04. sklearn-Simple-Linear-Regression-with-comments.ipynb 29.0 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/09. TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.6 kB11. Probability - Bayesian Inference/assets/12. Bayesian-Homework.pdf 27.9 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.6 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 27.4 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/06. Simple-Linear-Regression-with-sklearn-Exercise-Solution.ipynb 27.2 kB15. Statistics - Descriptive Statistics/assets/09. 2.6.Cross-table-and-scatter-plot.xlsx 26.7 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/04. sklearn-Simple-Linear-Regression.ipynb 26.7 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/02. 3.9.The-z-table.xlsx 26.2 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/03. 3.9.The-z-table.xlsx 26.2 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 26.2 kB64. Appendix - Additional Python Tools/assets/01. Additional-Python-Tools-Solutions.ipynb 26.1 kB64. Appendix - Additional Python Tools/assets/06. Additional-Python-Tools-Solutions.ipynb 26.1 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 26.1 kB15. Statistics - Descriptive Statistics/assets/19. 2.11.Covariance-lesson.xlsx 25.5 kB17. Statistics - Inferential Statistics Fundamentals/assets/05. 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.6 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 24.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/16. sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.6 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 22.3 kB
01. Part 1 Introduction/03. Download All Resources and Important FAQ.html 21.9 kB65. Appendix - pandas Fundamentals/assets/13. pandas-Fundamentals-Lectures.ipynb 21.8 kB65. Appendix - pandas Fundamentals/assets/01. pandas-Fundamentals-Lectures.ipynb 21.8 kB12. Probability - Distributions/15. A Practical Example of Probability Distributions.vtt 21.6 kB16. Statistics - Practical Example Descriptive Statistics/01. Practical Example Descriptive Statistics.vtt 21.5 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 21.1 kB40. ChatGPT for Data Science/assets/17. Movies-Data-Base-Recommendation-Engine.ipynb 20.9 kB14. Part 3 Statistics/assets/01. Statistics-Glossary.xlsx 20.8 kB15. Statistics - Descriptive Statistics/assets/20. 2.11.Covariance-exercise.xlsx 20.7 kB12. Probability - Distributions/assets/15. Daily-Views-post.xlsx 20.7 kB11. Probability - Bayesian Inference/12. A Practical Example of Bayesian Inference.vtt 20.6 kB15. Statistics - Descriptive Statistics/assets/01. Glossary.xlsx 20.4 kB15. Statistics - Descriptive Statistics/assets/14. 2.8.Skewness-exercise-solution.xlsx 20.2 kB53. Deep Learning - Business Case Example/assets/08. TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 20.2 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/assets/12. user-courses-review-test-set.csv 20.1 kB36. Advanced Statistical Methods - Logistic Regression/assets/11. Bank-data.csv 20.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/13. Bank-data.csv 20.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/16. Bank-data.csv 20.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/08. Bank-data.csv 20.0 kB17. Statistics - Inferential Statistics Fundamentals/assets/02. 3.2.What-is-a-distribution-lesson.xlsx 19.9 kB15. Statistics - Descriptive Statistics/assets/07. 2.5.The-Histogram-lesson.xlsx 19.1 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/12. Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb 18.4 kB39. Advanced Statistical Methods - Other Types of Clustering/assets/03. Heatmaps-with-comments.ipynb 18.1 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb 18.1 kB15. Statistics - Descriptive Statistics/assets/08. 2.5.The-Histogram-exercise-solution.xlsx 17.5 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 17.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/15. SKLEAR-1.IPY 17.2 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. TensorFlow-MNIST-All-Exercises.ipynb 17.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/12. sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb 17.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/17. sklearn-Feature-Scaling-Exercise-Solution.ipynb 16.7 kB15. Statistics - Descriptive Statistics/assets/10. 2.6.Cross-table-and-scatter-plot-exercise.xlsx 16.7 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/07. 3.11.The-t-table.xlsx 16.2 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/06. 3.11.The-t-table.xlsx 16.2 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 16.2 kB12. Probability - Distributions/assets/15. Customers-Membership-post.xlsx 16.0 kB15. Statistics - Descriptive Statistics/assets/08. 2.5.The-Histogram-exercise.xlsx 15.9 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/10. TensorFlow-MNIST-Exercises-All.ipynb 15.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/13. sklearn-Multiple-Linear-Regression-Exercise-Solution.ipynb 15.8 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.7 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 15.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/15. Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.7 kB15. Statistics - Descriptive Statistics/assets/04. 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.6 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.6 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 15.5 kB10. Probability - Combinatorics/11. A Practical Example of Combinatorics.vtt 15.5 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 15.5 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 15.5 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.4 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/15. sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2.ipynb 15.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.2 kB35. Advanced Statistical Methods - Practical Example Linear Regression/01. Practical Example Linear Regression (Part 1).vtt 15.2 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 1.TensorFlow-MNIST-Width-Solution.ipynb 15.2 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 15.1 kB20. Statistics - Hypothesis Testing/assets/08. 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.9 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/12. TensorFlow-MNIST-complete-with-comments.ipynb 14.9 kB20. Statistics - Hypothesis Testing/assets/11. 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.7 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/11. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/12. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.6 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.6 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 14.5 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 14.4 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 1.TensorFlow-MNIST-Width-Solution.ipynb 14.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.3 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/10. TensorFlow-Minimal-Example-All-Exercises.ipynb 14.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.3 kB19. Statistics - Practical Example Inferential Statistics/01. Practical Example Inferential Statistics.vtt 14.2 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 14.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/12. sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 14.0 kB53. Deep Learning - Business Case Example/04. Business Case Preprocessing the Data.vtt 13.9 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/04. Business Case Preprocessing.vtt 13.9 kB65. Appendix - pandas Fundamentals/assets/13. Location.csv 13.8 kB65. Appendix - pandas Fundamentals/assets/01. Location.csv 13.8 kB64. Appendix - Additional Python Tools/assets/06. Additional-Python-Tools-Lectures.ipynb 13.8 kB64. Appendix - Additional Python Tools/assets/01. Additional-Python-Tools-Lectures.ipynb 13.8 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/03. Multiple-Linear-Regression-Exercise-Solution.ipynb 13.7 kB15. Statistics - Descriptive Statistics/assets/06. 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.5 kB02. The Field of Data Science - The Various Data Science Disciplines/04. Continuing with BI, ML, and AI.vtt 13.4 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/09. 12.9.TensorFlow-MNIST-with-comments.ipynb 13.3 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/10. sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.3 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/05. Minimal-example-All-Exercises.ipynb 13.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/14. SKLEAR-1.IPY 13.2 kB64. Appendix - Additional Python Tools/05. List Comprehensions.vtt 13.1 kB20. Statistics - Hypothesis Testing/assets/11. 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 13.1 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/08. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/09. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/11. sklearn-How-to-properly-include-p-values.ipynb 13.0 kB64. Appendix - Additional Python Tools/01. Using the .format() Method.vtt 13.0 kB20. Statistics - Hypothesis Testing/assets/09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.9 kB15. Statistics - Descriptive Statistics/assets/18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.9 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/10. TensorFlow-MNIST-Part6-with-comments.ipynb 12.8 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/09. 5.6.TensorFlow-Minimal-example-complete.ipynb 12.4 kB17. Statistics - Inferential Statistics Fundamentals/assets/05. 3.4.Standard-normal-distribution-exercise.xlsx 12.3 kB53. Deep Learning - Business Case Example/assets/11. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB53. Deep Learning - Business Case Example/assets/12. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB35. Advanced Statistical Methods - Practical Example Linear Regression/06. Practical Example Linear Regression (Part 4).vtt 12.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/14. sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 12.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/12. Accuracy-with-comments.ipynb 12.0 kB15. Statistics - Descriptive Statistics/assets/18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.9 kB42. Part 6 Mathematics/11. Why is Linear Algebra Useful.vtt 11.8 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/08. 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.8 kB05. The Field of Data Science - Popular Data Science Techniques/07. Techniques for Working with Traditional Methods.vtt 11.8 kB15. Statistics - Descriptive Statistics/assets/05. 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.7 kB64. Appendix - Additional Python Tools/assets/06. Additional-Python-Tools-Exercises.ipynb 11.7 kB64. Appendix - Additional Python Tools/assets/01. Additional-Python-Tools-Exercises.ipynb 11.7 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/04. Minimal-example-Part-4-Complete.ipynb 11.7 kB20. Statistics - Hypothesis Testing/assets/15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.7 kB15. Statistics - Descriptive Statistics/assets/12. 2.7.Mean-median-and-mode-exercise-solution.xlsx 11.6 kB20. Statistics - Hypothesis Testing/assets/09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.6 kB20. Statistics - Hypothesis Testing/assets/13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.5 kB20. Statistics - Hypothesis Testing/assets/06. 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.5 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/02. 3.9.Population-variance-known-z-score-lesson.xlsx 11.5 kB53. Deep Learning - Business Case Example/assets/04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/12. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/11. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/03. 3.9.Population-variance-known-z-score-exercise-solution.xlsx 11.4 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/07. 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.4 kB35. Advanced Statistical Methods - Practical Example Linear Regression/08. Practical Example Linear Regression (Part 5).vtt 11.3 kB15. Statistics - Descriptive Statistics/assets/16. 2.9.Variance-exercise-solution.xlsx 11.3 kB20. Statistics - Hypothesis Testing/assets/06. 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.3 kB53. Deep Learning - Business Case Example/01. Business Case Exploring the Dataset and Identifying Predictors.vtt 11.3 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/09. TensorFlow-MNIST-Part5-with-comments.ipynb 11.2 kB15. Statistics - Descriptive Statistics/assets/17. 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.2 kB05. The Field of Data Science - Popular Data Science Techniques/01. Techniques for Working with Traditional Data.vtt 11.2 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/04. Basic NN Example (Part 4).vtt 11.2 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/01. Business Case Getting Acquainted with the Dataset.vtt 11.2 kB20. Statistics - Hypothesis Testing/assets/05. 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.2 kB05. The Field of Data Science - Popular Data Science Techniques/10. Types of Machine Learning.vtt 11.2 kB58. Software Integration/03. Taking a Closer Look at APIs.vtt 11.2 kB15. Statistics - Descriptive Statistics/assets/12. 2.7.Mean-median-and-mode-exercise.xlsx 11.1 kB65. Appendix - pandas Fundamentals/01. Introduction to pandas Series.vtt 11.1 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/03. 3.9.Population-variance-known-z-score-exercise.xlsx 11.1 kB15. Statistics - Descriptive Statistics/assets/16. 2.9.Variance-exercise.xlsx 11.1 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/06. 3.11.Population-variance-unknown-t-score-lesson.xlsx 11.0 kB20. Statistics - Hypothesis Testing/assets/13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11.0 kB38. Advanced Statistical Methods - K-Means Clustering/assets/15. Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 11.0 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/08. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/09. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/07. 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.9 kB60. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.vtt 10.8 kB20. Statistics - Hypothesis Testing/assets/15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.8 kB60. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.vtt 10.8 kB65. Appendix - pandas Fundamentals/11. Data Selection in pandas DataFrames.vtt 10.8 kB15. Statistics - Descriptive Statistics/assets/11. 2.7.Mean-median-and-mode-lesson.xlsx 10.7 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/08. TensorFlow-MNIST-Part4-with-comments.ipynb 10.7 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/09. 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/08. MNIST Learning.vtt 10.7 kB64. Appendix - Additional Python Tools/06. Anonymous (Lambda) Functions.vtt 10.7 kB12. Probability - Distributions/02. Types of Probability Distributions.vtt 10.7 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/10. sklearn-Feature-Selection-with-F-regression.ipynb 10.7 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/08. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.7 kB17. Statistics - Inferential Statistics Fundamentals/assets/04. 3.4.Standard-normal-distribution-lesson.xlsx 10.6 kB28. Python - Sequences/01. Lists.vtt 10.6 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/07. TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.6 kB38. Advanced Statistical Methods - K-Means Clustering/assets/05. Categorical.csv 10.6 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/09. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.6 kB63. Case Study - Analyzing the Predicted Outputs in Tableau/02. Analyzing Age vs Probability in Tableau.vtt 10.5 kB65. Appendix - pandas Fundamentals/assets/13. Region.csv 10.5 kB65. Appendix - pandas Fundamentals/assets/01. Region.csv 10.5 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.4 kB15. Statistics - Descriptive Statistics/assets/15. 2.9.Variance-lesson.xlsx 10.3 kB13. Probability - Probability in Other Fields/01. Probability in Finance.vtt 10.3 kB53. Deep Learning - Business Case Example/assets/09. TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.3 kB53. Deep Learning - Business Case Example/assets/05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/09. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 10.1 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/11. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 10.1 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 10.1 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 10.0 kB20. Statistics - Hypothesis Testing/assets/10. 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB38. Advanced Statistical Methods - K-Means Clustering/02. A Simple Example of Clustering.vtt 10.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.vtt 9.9 kB12. Probability - Distributions/assets/15. Customers-Membership.xlsx 9.9 kB63. Case Study - Analyzing the Predicted Outputs in Tableau/04. Analyzing Reasons vs Probability in Tableau.vtt 9.9 kB20. Statistics - Hypothesis Testing/assets/12. 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB02. The Field of Data Science - The Various Data Science Disciplines/03. Business Analytics, Data Analytics, and Data Science An Introduction.vtt 9.9 kB18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.vtt 9.8 kB52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.vtt 9.8 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. Dealing with Categorical Data - Dummy Variables.vtt 9.8 kB12. Probability - Distributions/assets/15. Daily-Views.xlsx 9.8 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/13. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB15. Statistics - Descriptive Statistics/assets/14. 2.8.Skewness-exercise.xlsx 9.7 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/13. Making-predictions-with-comments.ipynb 9.6 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/07. TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB05. The Field of Data Science - Popular Data Science Techniques/09. Machine Learning (ML) Techniques.vtt 9.5 kB20. Statistics - Hypothesis Testing/assets/14. 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB03. The Field of Data Science - Connecting the Data Science Disciplines/01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 9.5 kB12. Probability - Distributions/08. Characteristics of Continuous Distributions.vtt 9.4 kB18. Statistics - Inferential Statistics Confidence Intervals/assets/14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.vtt 9.4 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.vtt 9.4 kB38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).vtt 9.4 kB13. Probability - Probability in Other Fields/02. Probability in Statistics.vtt 9.3 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/08. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB42. Part 6 Mathematics/10. Dot Product of Matrices.vtt 9.3 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/06. TensorFlow-Minimal-example-Part2.ipynb 9.3 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/19. sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB09. Part 2 Probability/01. The Basic Probability Formula.vtt 9.2 kB28. Python - Sequences/05. Dictionaries.vtt 9.1 kB60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.vtt 9.1 kB05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.vtt 9.0 kB12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.vtt 9.0 kB44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.vtt 9.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).vtt 9.0 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.vtt 8.9 kB28. Python - Sequences/02. Using Methods.vtt 8.9 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/07. sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/08. 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.vtt 8.8 kB21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.vtt 8.8 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/07. TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB53. Deep Learning - Business Case Example/assets/05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB29. Python - Iterations/03. Lists with the range() Function.vtt 8.8 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.vtt 8.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/07. 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB64. Appendix - Additional Python Tools/03. Introduction to Nested For Loops.vtt 8.7 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/32. Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/07. How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.vtt 8.7 kB64. Appendix - Additional Python Tools/04. Triple Nested For Loops.vtt 8.7 kB18. Statistics - Inferential Statistics Confidence Intervals/09. Confidence intervals. Two means. Dependent samples.vtt 8.7 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/10. Machine Learning with Naïve Bayes (First Attempt).vtt 8.6 kB12. Probability - Distributions/01. Fundamentals of Probability Distributions.vtt 8.6 kB46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.vtt 8.6 kB65. Appendix - pandas Fundamentals/12. pandas DataFrames - Indexing with .iloc[].vtt 8.5 kB35. Advanced Statistical Methods - Practical Example Linear Regression/02. Practical Example Linear Regression (Part 2).vtt 8.5 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/29. Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB36. Advanced Statistical Methods - Logistic Regression/assets/16. Bank-data-testing.csv 8.5 kB38. Advanced Statistical Methods - K-Means Clustering/assets/03. Countries-exercise.csv 8.5 kB38. Advanced Statistical Methods - K-Means Clustering/assets/07. Countries-exercise.csv 8.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/06. Creating a Data Provider.vtt 8.4 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/05. First Regression in Python.vtt 8.4 kB60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.vtt 8.4 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.vtt 8.4 kB15. Statistics - Descriptive Statistics/15. Variance.vtt 8.4 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/01. The Linear Regression Model.vtt 8.3 kB53. Deep Learning - Business Case Example/09. Business Case Setting an Early Stopping Mechanism.vtt 8.3 kB20. Statistics - Hypothesis Testing/05. Test for the Mean. Population Variance Known.vtt 8.3 kB62. Case Study - Loading the 'absenteeism_module'/03. Deploying the 'absenteeism_module' - Part II.vtt 8.2 kB29. Python - Iterations/04. Conditional Statements and Loops.vtt 8.2 kB65. Appendix - pandas Fundamentals/09. Introduction to pandas DataFrames - Part II.vtt 8.2 kB60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.vtt 8.2 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 8.2 kB06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.vtt 8.2 kB52. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.vtt 8.1 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/03. Tokenization and Vectorization.vtt 8.1 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/06. 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB22. Part 4 Introduction to Python/06. Prerequisites for Coding in the Jupyter Notebooks.vtt 8.1 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/09. Basic NN Example with TF Model Output.vtt 8.0 kB29. Python - Iterations/06. How to Iterate over Dictionaries.vtt 8.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/07. sklearn-Multiple-Linear-Regression.ipynb 8.0 kB44. Deep Learning - Introduction to Neural Networks/12. Optimization Algorithm n-Parameter Gradient Descent.vtt 8.0 kB05. The Field of Data Science - Popular Data Science Techniques/11. Evolution and Latest Trends of Machine Learning (ML).vtt 7.9 kB11. Probability - Bayesian Inference/11. Bayes' Law.vtt 7.9 kB23. Python - Variables and Data Types/03. Python Strings.vtt 7.8 kB63. Case Study - Analyzing the Predicted Outputs in Tableau/06. Analyzing Transportation Expense vs Probability in Tableau.vtt 7.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.vtt 7.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/03. Simple Linear Regression with sklearn.vtt 7.8 kB39. Advanced Statistical Methods - Other Types of Clustering/02. Dendrogram.vtt 7.8 kB38. Advanced Statistical Methods - K-Means Clustering/06. How to Choose the Number of Clusters.vtt 7.7 kB40. ChatGPT for Data Science/10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.vtt 7.7 kB36. Advanced Statistical Methods - Logistic Regression/assets/15. Testing-the-model-with-comments.ipynb 7.7 kB23. Python - Variables and Data Types/assets/03. Strings-Lecture-Py3.ipynb 7.7 kB38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).vtt 7.7 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/02. Adjusted R-Squared.vtt 7.7 kB28. Python - Sequences/04. Tuples.vtt 7.7 kB40. ChatGPT for Data Science/assets/04. Data-Preprocessing-Medical-Data.ipynb 7.7 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.vtt 7.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/06. Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB40. ChatGPT for Data Science/19. Using ChatGPT for ethical considerations.vtt 7.7 kB40. ChatGPT for Data Science/09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.vtt 7.6 kB02. The Field of Data Science - The Various Data Science Disciplines/01. Data Science and Business Buzzwords Why are there so Many.vtt 7.5 kB38. Advanced Statistical Methods - K-Means Clustering/assets/14. Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB65. Appendix - pandas Fundamentals/08. Introduction to pandas DataFrames - Part I.vtt 7.5 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/29. Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/05. 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/06. Fitting the Model and Assessing its Accuracy.vtt 7.5 kB22. Part 4 Introduction to Python/01. Introduction to Programming.vtt 7.5 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/19. sklearn-Train-Test-Split.ipynb 7.4 kB65. Appendix - pandas Fundamentals/03. Working with Methods in Python - Part I.vtt 7.4 kB40. ChatGPT for Data Science/01. Traditional data science methods and the role of ChatGPT.vtt 7.4 kB12. Probability - Distributions/07. Discrete Distributions The Poisson Distribution.vtt 7.4 kB22. Part 4 Introduction to Python/02. Why Python.vtt 7.3 kB20. Statistics - Hypothesis Testing/01. Null vs Alternative Hypothesis.vtt 7.3 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/07. Business Case Model Outline.vtt 7.3 kB52. Deep Learning - Classifying on the MNIST Dataset/08. MNIST Outline the Model.vtt 7.3 kB60. Case Study - Preprocessing the 'Absenteeism_data'/03. Checking the Content of the Data Set.vtt 7.3 kB09. Part 2 Probability/04. Events and Their Complements.vtt 7.3 kB13. Probability - Probability in Other Fields/03. Probability in Data Science.vtt 7.3 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/11. Dummy-variables-with-comments.ipynb 7.3 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/08. A3 Normality and Homoscedasticity.vtt 7.2 kB58. Software Integration/05. Software Integration - Explained.vtt 7.2 kB48. Deep Learning - Overfitting/06. Early Stopping or When to Stop Training.vtt 7.1 kB09. Part 2 Probability/02. Computing Expected Values.vtt 7.1 kB09. Part 2 Probability/03. Frequency.vtt 7.1 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.vtt 7.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).vtt 7.1 kB02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.vtt 7.1 kB15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.vtt 7.1 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.vtt 7.1 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.vtt 7.1 kB02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.vtt 7.0 kB30. Python - Advanced Python Tools/01. Object Oriented Programming.vtt 7.0 kB01. Part 1 Introduction/01. A Practical Example What You Will Learn in This Course.vtt 7.0 kB26. Python - Conditional Statements/03. The ELIF Statement.vtt 7.0 kB38. Advanced Statistical Methods - K-Means Clustering/assets/12. Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/11. R-Squared.vtt 7.0 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/03. Minimal-example-Part-3.ipynb 7.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/16. Testing-the-Model-Exercise.ipynb 7.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt 6.9 kB20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.vtt 6.9 kB38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.vtt 6.9 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/12. TensorFlow-MNIST-complete.ipynb 6.9 kB29. Python - Iterations/01. For Loops.vtt 6.9 kB46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.vtt 6.9 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/11. Machine Learning with Naïve Bayes – converting the problem to a binary one.vtt 6.9 kB15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.vtt 6.9 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/02. Basic NN Example (Part 2).vtt 6.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.vtt 6.8 kB62. Case Study - Loading the 'absenteeism_module'/assets/01. absenteeism-module.py 6.8 kB38. Advanced Statistical Methods - K-Means Clustering/01. K-Means Clustering.vtt 6.8 kB46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.vtt 6.7 kB65. Appendix - pandas Fundamentals/10. pandas DataFrames - Common Attributes.vtt 6.7 kB36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.vtt 6.7 kB04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.vtt 6.7 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.vtt 6.7 kB52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.vtt 6.7 kB40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.vtt 6.6 kB18. Statistics - Inferential Statistics Confidence Intervals/08. Margin of Error.vtt 6.6 kB64. Appendix - Additional Python Tools/02. Iterating Over Range Objects.vtt 6.6 kB40. ChatGPT for Data Science/04. Data Preprocessing with ChatGPT.vtt 6.6 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/05. TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.vtt 6.5 kB54. Deep Learning - Conclusion/04. An overview of CNNs.vtt 6.5 kB40. ChatGPT for Data Science/17. Algorithm recommendation recommendation engine for movies with ChatGPT.vtt 6.5 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.vtt 6.5 kB15. Statistics - Descriptive Statistics/17. Standard Deviation and Coefficient of Variation.vtt 6.5 kB53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.vtt 6.5 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.vtt 6.5 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt 6.5 kB58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.vtt 6.4 kB18. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Independent Samples (Part 1).vtt 6.4 kB38. Advanced Statistical Methods - K-Means Clustering/09. To Standardize or not to Standardize.vtt 6.4 kB11. Probability - Bayesian Inference/04. Union of Sets.vtt 6.4 kB37. Advanced Statistical Methods - Cluster Analysis/02. Some Examples of Clusters.vtt 6.4 kB44. Deep Learning - Introduction to Neural Networks/01. Introduction to Neural Networks.vtt 6.4 kB36. Advanced Statistical Methods - Logistic Regression/assets/05. Example-bank-data.csv 6.4 kB42. Part 6 Mathematics/04. Arrays in Python - A Convenient Way To Represent Matrices.vtt 6.4 kB39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.vtt 6.3 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/07. 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB28. Python - Sequences/assets/05. Dictionaries-Solution-Py3.ipynb 6.3 kB51. Deep Learning - Preprocessing/03. Standardization.vtt 6.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/04. 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.vtt 6.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/17. sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/03. sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB29. Python - Iterations/02. While Loops and Incrementing.vtt 6.2 kB10. Probability - Combinatorics/06. Solving Combinations.vtt 6.2 kB60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.vtt 6.2 kB40. ChatGPT for Data Science/assets/19. friendships.csv 6.1 kB15. Statistics - Descriptive Statistics/11. Mean, median and mode.vtt 6.1 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.vtt 6.1 kB25. Python - Other Python Operators/02. Logical and Identity Operators.vtt 6.1 kB52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.vtt 6.1 kB20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.vtt 6.1 kB60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt 6.1 kB36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.vtt 6.0 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.vtt 6.0 kB11. Probability - Bayesian Inference/07. The Conditional Probability Formula.vtt 6.0 kB17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.vtt 6.0 kB38. Advanced Statistical Methods - K-Means Clustering/assets/11. Market-segmentation-example-with-comments.ipynb 6.0 kB48. Deep Learning - Overfitting/01. What is Overfitting.vtt 6.0 kB25. Python - Other Python Operators/assets/02. Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB65. Appendix - pandas Fundamentals/06. Using .unique() and .nunique().vtt 6.0 kB14. Part 3 Statistics/01. Population and Sample.vtt 6.0 kB05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.vtt 6.0 kB40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.vtt 6.0 kB58. Software Integration/04. Communication between Software Products through Text Files.vtt 6.0 kB15. Statistics - Descriptive Statistics/01. Types of Data.vtt 6.0 kB38. Advanced Statistical Methods - K-Means Clustering/assets/02. Country-clusters-with-comments.ipynb 5.9 kB65. Appendix - pandas Fundamentals/05. Parameters and Arguments in pandas.vtt 5.9 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/13. Making-predictions.ipynb 5.9 kB36. Advanced Statistical Methods - Logistic Regression/assets/15. Testing-the-model.ipynb 5.9 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.vtt 5.9 kB54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.vtt 5.9 kB12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.vtt 5.9 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/13. sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB40. ChatGPT for Data Science/12. Hypothesis testing with ChatGPT.vtt 5.8 kB38. Advanced Statistical Methods - K-Means Clustering/assets/04. Categorical-data-with-comments.ipynb 5.8 kB18. Statistics - Inferential Statistics Confidence Intervals/04. Confidence Interval Clarifications.vtt 5.7 kB17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.vtt 5.7 kB40. ChatGPT for Data Science/assets/12. Students-Hypothesis-Testing.ipynb 5.7 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/07. A2 No Endogeneity.vtt 5.7 kB36. Advanced Statistical Methods - Logistic Regression/07. Understanding Logistic Regression Tables.vtt 5.7 kB65. Appendix - pandas Fundamentals/07. Using .sort_values().vtt 5.7 kB53. Deep Learning - Business Case Example/assets/04. TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/04. TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 5.7 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt 5.7 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.vtt 5.7 kB65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].vtt 5.7 kB34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.vtt 5.7 kB42. Part 6 Mathematics/08. Transpose of a Matrix.vtt 5.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/07. How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.vtt 5.7 kB28. Python - Sequences/03. List Slicing.vtt 5.7 kB27. Python - Python Functions/assets/07. Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB20. Statistics - Hypothesis Testing/04. Type I Error and Type II Error.vtt 5.6 kB46. Deep Learning - TensorFlow 2.0 Introduction/02. TensorFlow Outline and Comparison with Other Libraries.vtt 5.6 kB44. Deep Learning - Introduction to Neural Networks/03. Types of Machine Learning.vtt 5.6 kB54. Deep Learning - Conclusion/01. Summary on What You've Learned.vtt 5.6 kB20. Statistics - Hypothesis Testing/12. Test for the mean. Independent Samples (Part 1).vtt 5.6 kB11. Probability - Bayesian Inference/01. Sets and Events.vtt 5.6 kB23. Python - Variables and Data Types/assets/03. Strings-Solution-Py3.ipynb 5.6 kB01. Part 1 Introduction/02. What Does the Course Cover.vtt 5.6 kB12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.vtt 5.5 kB18. Statistics - Inferential Statistics Confidence Intervals/06. Confidence Intervals; Population Variance Unknown; T-score.vtt 5.5 kB36. Advanced Statistical Methods - Logistic Regression/assets/13. Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).vtt 5.5 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.vtt 5.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.vtt 5.5 kB44. Deep Learning - Introduction to Neural Networks/10. Common Objective Functions Cross-Entropy Loss.vtt 5.5 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.vtt 5.5 kB05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.vtt 5.5 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.vtt 5.5 kB36. Advanced Statistical Methods - Logistic Regression/assets/02. Admittance-with-comments.ipynb 5.4 kB51. Deep Learning - Preprocessing/05. Binary and One-Hot Encoding.vtt 5.4 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/06. Calculating the Accuracy of the Model.vtt 5.4 kB20. Statistics - Hypothesis Testing/07. p-value.vtt 5.4 kB36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.vtt 5.4 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/05. Activation Functions.vtt 5.4 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt 5.4 kB12. Probability - Distributions/05. Discrete Distributions The Bernoulli Distribution.vtt 5.3 kB17. Statistics - Inferential Statistics Fundamentals/03. The Normal Distribution.vtt 5.3 kB40. ChatGPT for Data Science/06. Analyzing a client database with ChatGPT in Python.vtt 5.3 kB60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.vtt 5.3 kB36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.vtt 5.3 kB40. ChatGPT for Data Science/07. Analyzing a client database with ChatGPT in Python – analyzing top products.vtt 5.3 kB02. The Field of Data Science - The Various Data Science Disciplines/02. What is the difference between Analysis and Analytics.vtt 5.2 kB15. Statistics - Descriptive Statistics/19. Covariance.vtt 5.2 kB12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.vtt 5.2 kB02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.vtt 5.2 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.vtt 5.2 kB36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.vtt 5.2 kB39. Advanced Statistical Methods - Other Types of Clustering/01. Types of Clustering.vtt 5.2 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/05. Overcome Imbalanced Data in Machine Learning.vtt 5.2 kB28. Python - Sequences/assets/03. List-Slicing-Lecture-Py3.ipynb 5.1 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/09. A4 No Autocorrelation.vtt 5.1 kB48. Deep Learning - Overfitting/03. What is Validation.vtt 5.1 kB62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.vtt 5.1 kB15. Statistics - Descriptive Statistics/21. Correlation Coefficient.vtt 5.1 kB37. Advanced Statistical Methods - Cluster Analysis/01. Introduction to Cluster Analysis.vtt 5.1 kB10. Probability - Combinatorics/05. Solving Variations without Repetition.vtt 5.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/03. sklearn-Simple-Linear-Regression.ipynb 5.0 kB38. Advanced Statistical Methods - K-Means Clustering/assets/05. Clustering-Categorical-Data-Solution.ipynb 5.0 kB22. Part 4 Introduction to Python/04. Installing Python and Jupyter.vtt 5.0 kB30. Python - Advanced Python Tools/04. Importing Modules in Python.vtt 5.0 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/08. Basic NN Example with TF Loss Function and Gradient Descent.vtt 5.0 kB44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.vtt 5.0 kB15. Statistics - Descriptive Statistics/02. Levels of Measurement.vtt 4.9 kB43. Part 7 Deep Learning/01. What to Expect from this Part.vtt 4.9 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/01. Stochastic Gradient Descent.vtt 4.9 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/01. Exploring the Problem with a Machine Learning Mindset.vtt 4.9 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/23. Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB23. Python - Variables and Data Types/01. Variables.vtt 4.9 kB36. Advanced Statistical Methods - Logistic Regression/assets/08. Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.vtt 4.9 kB44. Deep Learning - Introduction to Neural Networks/02. Training the Model.vtt 4.9 kB11. Probability - Bayesian Inference/10. The Multiplication Law.vtt 4.8 kB18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent Samples (Part 2).vtt 4.8 kB38. Advanced Statistical Methods - K-Means Clustering/assets/12. Market-segmentation-example-Part2.ipynb 4.8 kB53. Deep Learning - Business Case Example/06. Business Case Load the Preprocessed Data.vtt 4.8 kB07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.vtt 4.8 kB38. Advanced Statistical Methods - K-Means Clustering/assets/03. A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB42. Part 6 Mathematics/01. What is a Matrix.vtt 4.7 kB11. Probability - Bayesian Inference/02. Ways Sets Can Interact.vtt 4.7 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/11. Dummy-Variables.ipynb 4.7 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A5 No Multicollinearity.vtt 4.7 kB53. Deep Learning - Business Case Example/assets/07. TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB28. Python - Sequences/assets/04. Tuples-Solution-Py3.ipynb 4.7 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/07. Backpropagation.vtt 4.7 kB60. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.vtt 4.7 kB38. Advanced Statistical Methods - K-Means Clustering/08. Pros and Cons of K-Means Clustering.vtt 4.7 kB18. Statistics - Inferential Statistics Confidence Intervals/05. Student's T Distribution.vtt 4.7 kB42. Part 6 Mathematics/assets/04. Scalars-Vectors-and-Matrices.ipynb 4.7 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/01. Basic NN Example (Part 1).vtt 4.6 kB38. Advanced Statistical Methods - K-Means Clustering/assets/06. Selecting-the-number-of-clusters.ipynb 4.6 kB27. Python - Python Functions/02. How to Create a Function with a Parameter.vtt 4.6 kB27. Python - Python Functions/assets/07. Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB36. Advanced Statistical Methods - Logistic Regression/assets/11. Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB10. Probability - Combinatorics/07. Symmetry of Combinations.vtt 4.6 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/06. Activation Functions Softmax Activation.vtt 4.6 kB35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).vtt 4.6 kB12. Probability - Distributions/13. Continuous Distributions The Exponential Distribution.vtt 4.6 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/09. Decomposition of Variability.vtt 4.6 kB38. Advanced Statistical Methods - K-Means Clustering/assets/14. Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB15. Statistics - Descriptive Statistics/05. Numerical Variables - Frequency Distribution Table.vtt 4.6 kB36. Advanced Statistical Methods - Logistic Regression/assets/05. Building-a-Logistic-Regression-Solution.ipynb 4.5 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.vtt 4.5 kB10. Probability - Combinatorics/02. Permutations and How to Use Them.vtt 4.5 kB36. Advanced Statistical Methods - Logistic Regression/09. What do the Odds Actually Mean.vtt 4.5 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/03. Basic NN Example (Part 3).vtt 4.5 kB28. Python - Sequences/assets/02. Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.5 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/03. The Importance of Working with a Balanced Dataset.vtt 4.5 kB37. Advanced Statistical Methods - Cluster Analysis/04. Math Prerequisites.vtt 4.5 kB48. Deep Learning - Overfitting/05. N-Fold Cross Validation.vtt 4.5 kB24. Python - Basic Python Syntax/01. Using Arithmetic Operators in Python.vtt 4.5 kB40. ChatGPT for Data Science/assets/05. Medical-Data-ML-Attempt.ipynb 4.5 kB40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.vtt 4.5 kB40. ChatGPT for Data Science/18. Ethical principles in data and AI utilization.vtt 4.5 kB28. Python - Sequences/assets/05. Dictionaries-Lecture-Py3.ipynb 4.5 kB42. Part 6 Mathematics/09. Dot Product.vtt 4.4 kB59. Case Study - What's Next in the Course/03. Introducing the Data Set.vtt 4.4 kB60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.vtt 4.4 kB66. Bonus Lecture/01. Bonus Lecture Next Steps.html 4.4 kB53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.vtt 4.4 kB46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.vtt 4.4 kB36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.vtt 4.4 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/04. Standardizing the Data.vtt 4.4 kB28. Python - Sequences/assets/03. List-Slicing-Solution-Py3.ipynb 4.4 kB27. Python - Python Functions/07. Built-in Functions in Python.vtt 4.4 kB34. Advanced Statistical Methods - Linear Regression with sklearn/07. Multiple Linear Regression with sklearn.vtt 4.4 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.vtt 4.3 kB42. Part 6 Mathematics/06. Addition and Subtraction of Matrices.vtt 4.3 kB24. Python - Basic Python Syntax/assets/01. Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB10. Probability - Combinatorics/09. Combinatorics in Real-Life The Lottery.vtt 4.3 kB22. Part 4 Introduction to Python/03. Why Jupyter.vtt 4.3 kB60. Case Study - Preprocessing the 'Absenteeism_data'/assets/32. Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB36. Advanced Statistical Methods - Logistic Regression/assets/04. Admittance-regression-tables-fixed-error.ipynb 4.2 kB42. Part 6 Mathematics/03. Linear Algebra and Geometry.vtt 4.2 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/06. Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.2 kB10. Probability - Combinatorics/08. Solving Combinations with Separate Sample Spaces.vtt 4.2 kB59. Case Study - What's Next in the Course/02. The Business Task.vtt 4.2 kB51. Deep Learning - Preprocessing/01. Preprocessing Introduction.vtt 4.2 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/05. Simple-linear-regression-with-comments.ipynb 4.2 kB60. Case Study - Preprocessing the 'Absenteeism_data'/02. Importing the Absenteeism Data in Python.vtt 4.1 kB17. Statistics - Inferential Statistics Fundamentals/04. The Standard Normal Distribution.vtt 4.1 kB46. Deep Learning - TensorFlow 2.0 Introduction/03. TensorFlow 1 vs TensorFlow 2.vtt 4.1 kB42. Part 6 Mathematics/02. Scalars and Vectors.vtt 4.1 kB17. Statistics - Inferential Statistics Fundamentals/08. Estimators and Estimates.vtt 4.1 kB52. Deep Learning - Classifying on the MNIST Dataset/assets/03. TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB54. Deep Learning - Conclusion/05. An Overview of RNNs.vtt 4.1 kB65. Appendix - pandas Fundamentals/04. Working with Methods in Python - Part II.vtt 4.0 kB11. Probability - Bayesian Inference/08. The Law of Total Probability.vtt 4.0 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/03. 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB30. Python - Advanced Python Tools/03. What is the Standard Library.vtt 4.0 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.vtt 3.9 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/10. What is the OLS.vtt 3.9 kB42. Part 6 Mathematics/05. What is a Tensor.vtt 3.9 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/08. Backpropagation Picture.vtt 3.9 kB38. Advanced Statistical Methods - K-Means Clustering/assets/11. Market-segmentation-example.ipynb 3.9 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/05. Simple-linear-regression.ipynb 3.9 kB23. Python - Variables and Data Types/assets/01. Variables-Solution-Py3.ipynb 3.9 kB10. Probability - Combinatorics/10. A Recap of Combinatorics.vtt 3.9 kB49. Deep Learning - Initialization/03. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt 3.9 kB38. Advanced Statistical Methods - K-Means Clustering/assets/05. Clustering-Categorical-Data-Exercise.ipynb 3.9 kB49. Deep Learning - Initialization/02. Types of Simple Initializations.vtt 3.9 kB10. Probability - Combinatorics/04. Solving Variations with Repetition.vtt 3.8 kB34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.vtt 3.8 kB23. Python - Variables and Data Types/02. Numbers and Boolean Values in Python.vtt 3.8 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/01. Intro to the Case Study.vtt 3.8 kB49. Deep Learning - Initialization/01. What is Initialization.vtt 3.8 kB15. Statistics - Descriptive Statistics/13. Skewness.vtt 3.8 kB22. Part 4 Introduction to Python/05. Understanding Jupyter's Interface - the Notebook Dashboard.vtt 3.8 kB26. Python - Conditional Statements/01. The IF Statement.vtt 3.8 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/06. Loading the Dataset and Preprocessing.vtt 3.8 kB44. Deep Learning - Introduction to Neural Networks/04. The Linear Model (Linear Algebraic Version).vtt 3.8 kB60. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.vtt 3.8 kB27. Python - Python Functions/assets/07. Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/02. Minimal-example-Part-2.ipynb 3.7 kB52. Deep Learning - Classifying on the MNIST Dataset/01. MNIST The Dataset.vtt 3.7 kB36. Advanced Statistical Methods - Logistic Regression/assets/12. Accuracy.ipynb 3.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/15. iris-with-answers.csv 3.7 kB52. Deep Learning - Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.vtt 3.7 kB38. Advanced Statistical Methods - K-Means Clustering/assets/03. A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB34. Advanced Statistical Methods - Linear Regression with sklearn/01. What is sklearn and How is it Different from Other Packages.vtt 3.7 kB37. Advanced Statistical Methods - Cluster Analysis/03. Difference between Classification and Clustering.vtt 3.7 kB23. Python - Variables and Data Types/assets/01. Variables-Lecture-Py3.ipynb 3.7 kB42. Part 6 Mathematics/assets/10. Dot-product-Part-2.ipynb 3.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/01. MNIST What is the MNIST Dataset.vtt 3.7 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/03. Momentum.vtt 3.7 kB27. Python - Python Functions/05. Conditional Statements and Functions.vtt 3.7 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/03. Selecting the Inputs for the Logistic Regression.vtt 3.7 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/05. MNIST Loss and Optimization Algorithm.vtt 3.7 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/06. Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB36. Advanced Statistical Methods - Logistic Regression/assets/02. Admittance.ipynb 3.6 kB24. Python - Basic Python Syntax/assets/01. Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB36. Advanced Statistical Methods - Logistic Regression/04. Building a Logistic Regression.vtt 3.6 kB40. ChatGPT for Data Science/assets/19. users.csv 3.6 kB11. Probability - Bayesian Inference/06. Dependence and Independence of Sets.vtt 3.6 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/01. Multiple Linear Regression.vtt 3.5 kB46. Deep Learning - TensorFlow 2.0 Introduction/05. Types of File Formats Supporting TensorFlow.vtt 3.5 kB48. Deep Learning - Overfitting/04. Training, Validation, and Test Datasets.vtt 3.5 kB40. ChatGPT for Data Science/assets/06. ratings.csv 3.5 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/06. Types of File Formats, supporting Tensors.vtt 3.5 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/07. Adam (Adaptive Moment Estimation).vtt 3.5 kB25. Python - Other Python Operators/assets/02. Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/02. How to Install TensorFlow 1.vtt 3.5 kB10. Probability - Combinatorics/03. Simple Operations with Factorials.vtt 3.5 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/12. real-estate-price-size-year-view.csv 3.5 kB15. Statistics - Descriptive Statistics/07. The Histogram.vtt 3.4 kB23. Python - Variables and Data Types/assets/02. Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/assets/06. 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB38. Advanced Statistical Methods - K-Means Clustering/assets/04. Categorical-data.ipynb 3.4 kB38. Advanced Statistical Methods - K-Means Clustering/04. Clustering Categorical Data.vtt 3.4 kB38. Advanced Statistical Methods - K-Means Clustering/assets/02. Country-clusters.ipynb 3.4 kB27. Python - Python Functions/assets/03. Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.4 kB40. ChatGPT for Data Science/assets/05. patients-preprocessed.csv 3.4 kB41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/04. Imbalanced Data Sets.vtt 3.3 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/02. What is a Deep Net.vtt 3.3 kB26. Python - Conditional Statements/02. The ELSE Statement.vtt 3.3 kB26. Python - Conditional Statements/assets/03. Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.3 kB23. Python - Variables and Data Types/assets/02. Numbers-and-Boolean-Values-Solution-Py3.ipynb 3.3 kB12. Probability - Distributions/11. Continuous Distributions The Students' T Distribution.vtt 3.3 kB18. Statistics - Inferential Statistics Confidence Intervals/01. What are Confidence Intervals.vtt 3.3 kB42. Part 6 Mathematics/assets/06. Adding-and-subtracting-matrices.ipynb 3.3 kB28. Python - Sequences/assets/01. Lists-Solution-Py3.ipynb 3.3 kB42. Part 6 Mathematics/assets/07. Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 kB36. Advanced Statistical Methods - Logistic Regression/assets/08. Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 kB36. Advanced Statistical Methods - Logistic Regression/06. An Invaluable Coding Tip.vtt 3.2 kB26. Python - Conditional Statements/04. A Note on Boolean Values.vtt 3.2 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/09. Business Case Interpretation.vtt 3.2 kB24. Python - Basic Python Syntax/assets/03. Reassign-Values-Lecture-Py3.ipynb 3.2 kB27. Python - Python Functions/03. Defining a Function in Python - Part II.vtt 3.1 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/05. OLS Assumptions.vtt 3.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.vtt 3.1 kB52. Deep Learning - Classifying on the MNIST Dataset/03. MNIST Importing the Relevant Packages and Loading the Data.vtt 3.1 kB52. Deep Learning - Classifying on the MNIST Dataset/09. MNIST Select the Loss and the Optimizer.vtt 3.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/02. How are we Going to Approach this Section.vtt 3.1 kB05. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Machine Learning (ML).vtt 3.1 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/12. Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.1 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/02. Problems with Gradient Descent.vtt 3.1 kB60. Case Study - Preprocessing the 'Absenteeism_data'/06. Using a Statistical Approach towards the Solution to the Exercise.vtt 3.1 kB12. Probability - Distributions/12. Continuous Distributions The Chi-Squared Distribution.vtt 3.1 kB65. Appendix - pandas Fundamentals/02. A Note on Completing the Upcoming Coding Exercises.html 3.0 kB29. Python - Iterations/assets/04. Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 kB44. Deep Learning - Introduction to Neural Networks/09. Common Objective Functions L2-norm Loss.vtt 3.0 kB40. ChatGPT for Data Science/assets/04. patients.csv 3.0 kB28. Python - Sequences/assets/05. Dictionaries-Exercise-Py3.ipynb 3.0 kB36. Advanced Statistical Methods - Logistic Regression/assets/05. Building-a-Logistic-Regression-Exercise.ipynb 3.0 kB28. Python - Sequences/assets/04. Tuples-Lecture-Py3.ipynb 3.0 kB12. Probability - Distributions/04. Discrete Distributions The Uniform Distribution.vtt 3.0 kB42. Part 6 Mathematics/assets/08. Tranpose-of-a-matrix.ipynb 3.0 kB29. Python - Iterations/assets/06. Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 kB51. Deep Learning - Preprocessing/04. Preprocessing Categorical Data.vtt 2.9 kB60. Case Study - Preprocessing the 'Absenteeism_data'/05. What's Regression Analysis - a Quick Refresher.html 2.9 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/07. MNIST Batching and Early Stopping.vtt 2.9 kB48. Deep Learning - Overfitting/02. Underfitting and Overfitting for Classification.vtt 2.9 kB28. Python - Sequences/assets/02. Help-Yourself-with-Methods-Solution-Py3.ipynb 2.9 kB44. Deep Learning - Introduction to Neural Networks/05. The Linear Model with Multiple Inputs.vtt 2.9 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/02. Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.9 kB28. Python - Sequences/assets/03. List-Slicing-Exercise-Py3.ipynb 2.9 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/06. Simple-Linear-Regression-Exercise.ipynb 2.8 kB11. Probability - Bayesian Inference/05. Mutually Exclusive Sets.vtt 2.8 kB40. ChatGPT for Data Science/13. Marvels comic book database Intro to Regular Expressions (RegEx).vtt 2.8 kB42. Part 6 Mathematics/07. Errors when Adding Matrices.vtt 2.8 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.vtt 2.8 kB28. Python - Sequences/assets/01. Lists-Lecture-Py3.ipynb 2.8 kB54. Deep Learning - Conclusion/02. What's Further out there in terms of Machine Learning.vtt 2.8 kB44. Deep Learning - Introduction to Neural Networks/07. Graphical Representation of Simple Neural Networks.vtt 2.8 kB60. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.vtt 2.7 kB11. Probability - Bayesian Inference/09. The Additive Rule.vtt 2.7 kB40. ChatGPT for Data Science/assets/10. properties.csv 2.7 kB47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/01. What is a Layer.vtt 2.7 kB27. Python - Python Functions/01. Defining a Function in Python.vtt 2.7 kB24. Python - Basic Python Syntax/assets/01. Arithmetic-Operators-Exercise-Py3.ipynb 2.7 kB23. Python - Variables and Data Types/assets/03. Strings-Exercise-Py3.ipynb 2.7 kB57. Appendix Deep Learning - TensorFlow 1 Business Case/02. Business Case Outlining the Solution.vtt 2.7 kB11. Probability - Bayesian Inference/03. Intersection of Sets.vtt 2.7 kB25. Python - Other Python Operators/01. Comparison Operators.vtt 2.6 kB12. Probability - Distributions/03. Characteristics of Discrete Distributions.vtt 2.6 kB36. Advanced Statistical Methods - Logistic Regression/assets/10. 2.02.Binary-predictors.csv 2.6 kB36. Advanced Statistical Methods - Logistic Regression/assets/11. Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.6 kB25. Python - Other Python Operators/assets/01. Comparison-Operators-Lecture-Py3.ipynb 2.6 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/06. A1 Linearity.vtt 2.5 kB36. Advanced Statistical Methods - Logistic Regression/assets/04. Admittance-regression-summary-error.ipynb 2.5 kB60. Case Study - Preprocessing the 'Absenteeism_data'/01. What to Expect from the Following Sections.html 2.5 kB29. Python - Iterations/05. Conditional Statements, Functions, and Loops.vtt 2.5 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/04. Test for Significance of the Model (F-Test).vtt 2.5 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/03. Multiple-Linear-Regression-Exercise.ipynb 2.5 kB40. ChatGPT for Data Science/03. How ChatGPT can boost your productivity.vtt 2.5 kB36. Advanced Statistical Methods - Logistic Regression/assets/10. Binary-predictors.ipynb 2.5 kB25. Python - Other Python Operators/assets/01. Comparison-Operators-Solution-Py3.ipynb 2.5 kB38. Advanced Statistical Methods - K-Means Clustering/assets/15. iris-dataset.csv 2.5 kB38. Advanced Statistical Methods - K-Means Clustering/assets/14. iris-dataset.csv 2.5 kB26. Python - Conditional Statements/assets/03. Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.5 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/03. real-estate-price-size-year.csv 2.4 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/17. real-estate-price-size-year.csv 2.4 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/13. real-estate-price-size-year.csv 2.4 kB60. Case Study - Preprocessing the 'Absenteeism_data'/14. Dropping a Dummy Variable from the Data Set.html 2.4 kB05. The Field of Data Science - Popular Data Science Techniques/02. Real Life Examples of Traditional Data.vtt 2.4 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/05. Actual Introduction to TensorFlow.vtt 2.4 kB31. Part 5 Advanced Statistical Methods in Python/01. Introduction to Regression Analysis.vtt 2.4 kB24. Python - Basic Python Syntax/07. Structuring with Indentation.vtt 2.4 kB38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.vtt 2.3 kB20. Statistics - Hypothesis Testing/02. Further Reading on Null and Alternative Hypothesis.html 2.3 kB23. Python - Variables and Data Types/assets/02. Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/03. A Note on Installing Packages in Anaconda.html 2.3 kB44. Deep Learning - Introduction to Neural Networks/08. What is the Objective Function.vtt 2.3 kB05. The Field of Data Science - Popular Data Science Techniques/06. Real Life Examples of Business Intelligence (BI).vtt 2.3 kB29. Python - Iterations/assets/03. Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 kB50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/05. Learning Rate Schedules Visualized.vtt 2.3 kB23. Python - Variables and Data Types/assets/01. Variables-Exercise-Py3.ipynb 2.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/11. MNIST Solutions.html 2.3 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/03. MNIST Relevant Packages.vtt 2.2 kB26. Python - Conditional Statements/assets/01. Introduction-to-the-If-Statement-Solution-Py3.ipynb 2.2 kB29. Python - Iterations/assets/06. Iterating-over-Dictionaries-Exercise-Py3.ipynb 2.2 kB24. Python - Basic Python Syntax/assets/06. Indexing-Elements-Solution-Py3.ipynb 2.2 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/02. Correlation vs Regression.vtt 2.2 kB56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10. MNIST Exercises.html 2.2 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/02. Multiple-linear-regression-and-Adjusted-R-squared.ipynb 2.2 kB61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/14. ARTICLE - A Note on 'pickling'.html 2.2 kB53. Deep Learning - Business Case Example/11. Business Case Testing the Model.vtt 2.2 kB28. Python - Sequences/assets/01. Lists-Exercise-Py3.ipynb 2.2 kB42. Part 6 Mathematics/assets/09. Dot-product.ipynb 2.2 kB24. Python - Basic Python Syntax/assets/03. Reassign-Values-Solution-Py3.ipynb 2.2 kB63. Case Study - Analyzing the Predicted Outputs in Tableau/assets/02. Absenteeism-predictions.csv 2.2 kB63. Case Study - Analyzing the Predicted Outputs in Tableau/assets/01. Absenteeism-predictions.csv 2.2 kB27. Python - Python Functions/04. How to Use a Function within a Function.vtt 2.1 kB29. Python - Iterations/assets/04. Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb 2.1 kB36. Advanced Statistical Methods - Logistic Regression/assets/04. Admittance-regression.ipynb 2.1 kB40. ChatGPT for Data Science/assets/12. students.csv 2.1 kB17. Statistics - Inferential Statistics Fundamentals/07. Standard error.vtt 2.1 kB42. Part 6 Mathematics/assets/05. Tensors.ipynb 2.1 kB28. Python - Sequences/assets/04. Tuples-Exercise-Py3.ipynb 2.1 kB40. ChatGPT for Data Science/02. How to install ChatGPT.vtt 2.1 kB18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent Samples (Part 3).vtt 2.1 kB27. Python - Python Functions/assets/03. Another-Way-to-Define-a-Function-Solution-Py3.ipynb 2.0 kB52. Deep Learning - Classifying on the MNIST Dataset/11. MNIST - Exercises.html 2.0 kB29. Python - Iterations/assets/04. Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb 2.0 kB24. Python - Basic Python Syntax/04. Add Comments.vtt 2.0 kB28. Python - Sequences/assets/02. Help-Yourself-with-Methods-Exercise-Py3.ipynb 2.0 kB53. Deep Learning - Business Case Example/02. Business Case Outlining the Solution.vtt 2.0 kB24. Python - Basic Python Syntax/02. The Double Equality Sign.vtt 1.9 kB29. Python - Iterations/assets/05. All-In-Solution-Py3.ipynb 1.9 kB05. The Field of Data Science - Popular Data Science Techniques/04. Real Life Examples of Big Data.vtt 1.9 kB62. Case Study - Loading the 'absenteeism_module'/assets/01. Absenteeism-new-data.csv 1.9 kB60. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.vtt 1.9 kB62. Case Study - Loading the 'absenteeism_module'/assets/01. scaler 1.9 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/06. real-estate-price-size.csv 1.9 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/06. real-estate-price-size.csv 1.9 kB51. Deep Learning - Preprocessing/02. Types of Basic Preprocessing.vtt 1.9 kB36. Advanced Statistical Methods - Logistic Regression/01. Introduction to Logistic Regression.vtt 1.9 kB39. Advanced Statistical Methods - Other Types of Clustering/assets/03. Heatmaps.ipynb 1.9 kB29. Python - Iterations/assets/01. For-Loops-Solution-Py3.ipynb 1.8 kB27. Python - Python Functions/assets/02. Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb 1.8 kB40. ChatGPT for Data Science/assets/06. products.csv 1.8 kB26. Python - Conditional Statements/assets/02. Add-an-Else-Statement-Lecture-Py3.ipynb 1.8 kB26. Python - Conditional Statements/assets/03. Else-If-for-Brief-Elif-Exercise-Py3.ipynb 1.8 kB29. Python - Iterations/assets/02. While-Loops-and-Incrementing-Solution-Py3.ipynb 1.8 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/03. Geometrical Representation of the Linear Regression Model.vtt 1.8 kB27. Python - Python Functions/assets/06. Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb 1.8 kB60. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.vtt 1.7 kB24. Python - Basic Python Syntax/assets/03. Reassign-Values-Exercise-Py3.ipynb 1.7 kB17. Statistics - Inferential Statistics Fundamentals/01. Introduction.vtt 1.7 kB24. Python - Basic Python Syntax/06. Indexing Elements.vtt 1.7 kB45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/05. Basic NN Example Exercises.html 1.7 kB46. Deep Learning - TensorFlow 2.0 Introduction/assets/05. TensorFlow-Minimal-example-Part1.ipynb 1.7 kB27. Python - Python Functions/assets/05. Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb 1.7 kB40. ChatGPT for Data Science/11. Assignment 1.html 1.7 kB29. Python - Iterations/assets/05. All-In-Lecture-Py3.ipynb 1.7 kB25. Python - Other Python Operators/assets/01. Comparison-Operators-Exercise-Py3.ipynb 1.6 kB55. Appendix Deep Learning - TensorFlow 1 Introduction/10. Basic NN Example with TF Exercises.html 1.6 kB27. Python - Python Functions/assets/04. 0.6.4-Using-a-Function-in-another-Function-Solution-Py3.ipynb 1.6 kB27. Python - Python Functions/assets/02. Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb 1.6 kB36. Advanced Statistical Methods - Logistic Regression/assets/02. 2.01.Admittance.csv 1.6 kB40. ChatGPT for Data Science/assets/06. customers.csv 1.6 kB32. Advanced Statistical Methods - Linear Regression with StatsModels/07. Using Seaborn for Graphs.vtt 1.6 kB40. ChatGPT for Data Science/15. Assignment 2.html 1.6 kB26. Python - Conditional Statements/assets/01. Introduction-to-the-If-Statement-Exercise-Py3.ipynb 1.6 kB24. Python - Basic Python Syntax/assets/05. Line-Continuation-Solution-Py3.ipynb 1.5 kB24. Python - Basic Python Syntax/assets/07. Structure-Your-Code-with-Indentation-Solution-Py3.ipynb 1.5 kB10. Probability - Combinatorics/01. Fundamentals of Combinatorics.vtt 1.5 kB30. Python - Advanced Python Tools/02. Modules and Packages.vtt 1.5 kB29. Python - Iterations/assets/03. Create-Lists-with-the-range-Function-Exercise-Py3.ipynb 1.5 kB24. Python - Basic Python Syntax/assets/02. The-Double-Equality-Sign-Lecture-Py3.ipynb 1.5 kB27. Python - Python Functions/06. Functions Containing a Few Arguments.vtt 1.5 kB46. Deep Learning - TensorFlow 2.0 Introduction/04. 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While-Loops-and-Incrementing-Exercise-Py3.ipynb 1.1 kB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/assets/02. 1.02.Multiple-linear-regression.csv 1.1 kB29. Python - Iterations/assets/02. While-Loops-and-Incrementing-Lecture-Py3.ipynb 1.1 kB29. Python - Iterations/assets/06. Iterating-over-Dictionaries-Lecture-Py3.ipynb 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/09. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/07. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/14. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/16. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/15. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/08. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/12. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/10. 1.02.Multiple-linear-regression.csv 1.1 kB34. Advanced Statistical Methods - Linear Regression with sklearn/assets/11. 1.02.Multiple-linear-regression.csv 1.1 kB27. Python - Python Functions/assets/05. Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb 1.1 kB54. Deep Learning - Conclusion/03. DeepMind and Deep Learning.html 1.1 kB27. Python - Python Functions/assets/04. 0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb 1.1 kB24. Python - Basic Python Syntax/assets/04. Add-Comments-Lecture-Py3.ipynb 1.1 kB26. Python - Conditional Statements/assets/02. Add-an-Else-Statement-Exercise-Py3.ipynb 1.0 kB62. Case Study - Loading the 'absenteeism_module'/assets/01. model 1.0 kB27. Python - Python Functions/assets/04. 0.6.4-Using-a-Function-in-another-Function-Lecture-Py3.ipynb 1.0 kB62. Case Study - Loading the 'absenteeism_module'/04. Exporting the Obtained Data Set as a .csv.html 998 Bytes62. Case Study - Loading the 'absenteeism_module'/assets/04. Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb 973 Bytes24. Python - Basic Python Syntax/assets/07. Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb 958 Bytes24. Python - Basic Python Syntax/assets/07. Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb 956 Bytes32. Advanced Statistical Methods - Linear Regression with StatsModels/assets/05. 1.01.Simple-linear-regression.csv 922 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/assets/04. 1.01.Simple-linear-regression.csv 922 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/assets/03. 1.01.Simple-linear-regression.csv 922 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/33. A Note on Exporting Your Data as a .csv File.html 883 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/08. EXERCISE - Dropping a Column from a DataFrame in Python.html 870 Bytes27. Python - Python Functions/assets/01. Defining-a-Function-in-Python-Lecture-Py3.ipynb 868 Bytes35. Advanced Statistical Methods - Practical Example Linear Regression/03. A Note on Multicollinearity.html 849 Bytes24. Python - Basic Python Syntax/assets/02. The-Double-Equality-Sign-Exercise-Py3.ipynb 838 Bytes26. Python - Conditional Statements/assets/04. A-Note-on-Boolean-Values-Lecture-Py3.ipynb 791 Bytes24. Python - Basic Python Syntax/assets/05. Line-Continuation-Lecture-Py3.ipynb 779 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/05. A Note on Normalization.html 733 Bytes35. Advanced Statistical Methods - Practical Example Linear Regression/07. Dummy Variables - Exercise.html 713 Bytes55. Appendix Deep Learning - TensorFlow 1 Introduction/01. READ ME!!!!.html 564 Bytes63. Case Study - Analyzing the Predicted Outputs in Tableau/05. EXERCISE - Transportation Expense vs Probability.html 553 Bytes47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/09. Backpropagation - A Peek into the Mathematics of Optimization.html 543 Bytes15. Statistics - Descriptive Statistics/16. Variance Exercise.html 522 Bytes62. Case Study - Loading the 'absenteeism_module'/01. Are You Sure You're All Set.html 519 Bytes35. Advanced Statistical Methods - Practical Example Linear Regression/09. Linear Regression - Exercise.html 503 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 478 Bytes57. Appendix Deep Learning - TensorFlow 1 Business Case/12. Business Case Final Exercise.html 443 Bytes53. Deep Learning - Business Case Example/12. Business Case Final Exercise.html 433 Bytes63. Case Study - Analyzing the Predicted Outputs in Tableau/03. EXERCISE - Reasons vs Probability.html 397 Bytes57. Appendix Deep Learning - TensorFlow 1 Business Case/05. Business Case Preprocessing Exercise.html 389 Bytes63. Case Study - Analyzing the Predicted Outputs in Tableau/01. EXERCISE - Age vs Probability.html 385 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/11. A Note on Calculation of P-values with sklearn.html 372 Bytes53. Deep Learning - Business Case Example/05. Business Case Preprocessing the Data - Exercise.html 370 Bytes36. Advanced Statistical Methods - Logistic Regression/assets/15. 2.03.Test-dataset.csv 322 Bytes61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15. EXERCISE - Saving the Model (and Scaler).html 284 Bytes38. Advanced Statistical Methods - K-Means Clustering/assets/11. 3.12.Example.csv 283 Bytes39. Advanced Statistical Methods - Other Types of Clustering/assets/03. Country-clusters-standardized.csv 244 Bytes38. Advanced Statistical Methods - K-Means Clustering/assets/02. 3.01.Country-clusters.csv 200 Bytes53. Deep Learning - Business Case Example/10. Setting an Early Stopping Mechanism - Exercise.html 192 Bytes
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-links/11. Logistic-Regression-prior-to-Backward-Elimination.url 191 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/18. EXERCISE - Using .concat() in Python.html 189 Bytes61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-links/09. Logistic-Regression-prior-to-Custom-Scaler.url 184 Bytes61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-links/15. Logistic-Regression-with-Comments.url 175 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167 Bytes61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-links/15. Logistic-Regression.url 161 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html 143 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 118 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html 117 Bytes60. Case Study - Preprocessing the 'Absenteeism_data'/09. SOLUTION - Dropping a Column from a DataFrame in Python.html 114 Bytes01. Part 1 Introduction/external-links/03. Download-all-resources.url 99 Bytes35. Advanced Statistical Methods - Practical Example Linear Regression/external-links/04. sklearn-Linear-Regression-Practical-Example-Part-3-.url 99 Bytes40. ChatGPT for Data Science/assets/05. diagnosis-mapping.csv 90 Bytes36. Advanced Statistical Methods - Logistic Regression/05. Building a Logistic Regression - Exercise.html 87 Bytes36. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes36. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html 87 Bytes36. Advanced Statistical Methods - Logistic Regression/08. Understanding Logistic Regression Tables - Exercise.html 87 Bytes36. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html 87 Bytes38. Advanced Statistical Methods - K-Means Clustering/05. Clustering Categorical Data - Exercise.html 87 Bytes38. Advanced Statistical Methods - K-Means Clustering/03. A Simple Example of Clustering - Exercise.html 87 Bytes38. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes38. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes38. Advanced Statistical Methods - K-Means Clustering/07. How to Choose the Number of Clusters - Exercise.html 87 Bytes42. Part 6 Mathematics/external-links/01. Math-Flashcards.url 87 Bytes15. Statistics - Descriptive Statistics/04. Categorical Variables Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/18. Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/06. Numerical Variables Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/10. Cross Tables and Scatter Plots Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/14. Skewness Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/20. Covariance Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/08. Histogram Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/12. Mean, Median and Mode Exercise.html 81 Bytes15. Statistics - Descriptive Statistics/22. Correlation Coefficient Exercise.html 81 Bytes16. Statistics - Practical Example Descriptive Statistics/02. Practical Example Descriptive Statistics Exercise.html 81 Bytes17. Statistics - Inferential Statistics Fundamentals/05. The Standard Normal Distribution Exercise.html 81 Bytes18. Statistics - Inferential Statistics Confidence Intervals/03. Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 Bytes18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 Bytes18. Statistics - Inferential Statistics Confidence Intervals/07. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 Bytes18. Statistics - Inferential Statistics Confidence Intervals/10. Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 Bytes19. Statistics - Practical Example Inferential Statistics/02. Practical Example Inferential Statistics Exercise.html 81 Bytes20. Statistics - Hypothesis Testing/13. Test for the mean. Independent Samples (Part 1). Exercise.html 81 Bytes20. Statistics - Hypothesis Testing/09. Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes20. Statistics - Hypothesis Testing/06. Test for the Mean. Population Variance Known Exercise.html 81 Bytes20. Statistics - Hypothesis Testing/15. Test for the mean. Independent Samples (Part 2). Exercise.html 81 Bytes20. Statistics - Hypothesis Testing/11. Test for the Mean. Dependent Samples Exercise.html 81 Bytes21. Statistics - Practical Example Hypothesis Testing/02. Practical Example Hypothesis Testing Exercise.html 81 Bytes52. Deep Learning - Classifying on the MNIST Dataset/07. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html 79 Bytes52. Deep Learning - Classifying on the MNIST Dataset/05. MNIST Preprocess the Data - Scale the Test Data - Exercise.html 79 Bytes53. Deep Learning - Business Case Example/07. Business Case Load the Preprocessed Data - Exercise.html 79 Bytes33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/03. Multiple Linear Regression Exercise.html 76 Bytes33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/12. Dealing with Categorical Data - Dummy Variables.html 76 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/06. Simple Linear Regression with sklearn - Exercise.html 76 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/09. Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/13. Multiple Linear Regression - Exercise.html 76 Bytes34. Advanced Statistical Methods - Linear Regression with sklearn/17. Feature Scaling (Standardization) - Exercise.html 76 Bytes35. Advanced Statistical Methods - Practical Example Linear Regression/05. Dummies and Variance Inflation Factor - Exercise.html 76 Bytes14. Part 3 Statistics/external-links/01. Statistics-Flashcards.url 51 Bytes09. Part 2 Probability/external-links/01. Probability-Flashcards.url 46 Bytes02. The Field of Data Science - The Various Data Science Disciplines/external-links/02. Intro-to-Data-Science-Flashcards.url 44 Bytes02. The Field of Data Science - The Various Data Science Disciplines/external-links/01. Intro-to-Data-Science-Flashcards.url 44 Bytes22. Part 4 Introduction to Python/external-links/01. Intro-to-Python-Flashcards.url 44 Bytes31. Part 5 Advanced Statistical Methods in Python/external-links/01. Advanced-Statistics-Flashcards.url 44 Bytes
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