[Tutorialsplanet.NET] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

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[Tutorialsplanet.NET] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

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文件列表

  • 16/1. Practical Example Descriptive Statistics.mp4 168.2 MB
  • 12. Probability - Distributions/29. A Practical Example of Probability Distributions.mp4 165.5 MB
  • 11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4 152.2 MB
  • 40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4 151.3 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 145.0 MB
  • 10. Probability - Combinatorics/20. A Practical Example of Combinatorics.mp4 140.8 MB
  • 3/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 133.0 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 131.2 MB
  • 56. Software Integration/5. Taking a Closer Look at APIs.mp4 121.2 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 117.1 MB
  • 2/7. Continuing with BI, ML, and AI.mp4 114.3 MB
  • 56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 109.1 MB
  • 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 108.5 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4 108.4 MB
  • 19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 107.6 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 104.1 MB
  • 13. Probability - Probability in Other Fields/1. Probability in Finance.mp4 103.9 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp4 101.8 MB
  • 35/1. Practical Example Linear Regression (Part 1).mp4 101.8 MB
  • 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 96.5 MB
  • 12. Probability - Distributions/3. Types of Probability Distributions.mp4 96.0 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 94.3 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting acquainted with the dataset.mp4 91.9 MB
  • 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 90.7 MB
  • 9. Part 2 Probability/1. The Basic Probability Formula.mp4 90.1 MB
  • 51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp4 88.4 MB
  • 12. Probability - Distributions/15. Characteristics of Continuous Distributions.mp4 88.2 MB
  • 20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 86.6 MB
  • 2/1. Data Science and Business Buzzwords Why are there so many.mp4 85.4 MB
  • 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 85.1 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 85.0 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 82.0 MB
  • 13. Probability - Probability in Other Fields/2. Probability in Statistics.mp4 81.0 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp4 80.1 MB
  • 9. Part 2 Probability/3. Computing Expected Values.mp4 79.4 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 79.2 MB
  • 22. Part 4 Introduction to Python/3. Why Python.mp4 78.7 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 78.2 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 78.1 MB
  • 12. Probability - Distributions/1. Fundamentals of Probability Distributions.mp4 77.0 MB
  • 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 76.4 MB
  • 15. Statistics - Descriptive Statistics/1. Types of Data.mp4 76.0 MB
  • 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 75.0 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4 73.9 MB
  • 21/1. Practical Example Hypothesis Testing.mp4 72.9 MB
  • 56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 72.4 MB
  • 12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.mp4 72.2 MB
  • 2/9. A Breakdown of our Data Science Infographic.mp4 71.0 MB
  • 51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp4 69.5 MB
  • 2/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 67.6 MB
  • 56. Software Integration/9. Software Integration - Explained.mp4 66.8 MB
  • 13. Probability - Probability in Other Fields/3. Probability in Data Science.mp4 66.6 MB
  • 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 65.9 MB
  • 54/9. MNIST Results and Testing.mp4 65.8 MB
  • 1/2. What Does the Course Cover.mp4 65.3 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 64.9 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 64.8 MB
  • 9. Part 2 Probability/5. Frequency.mp4 64.7 MB
  • 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 64.6 MB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 64.1 MB
  • 56. Software Integration/7. Communication between Software Products through Text Files.mp4 63.3 MB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 62.2 MB
  • 45/3. Digging into a Deep Net.mp4 62.2 MB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4 62.0 MB
  • 9. Part 2 Probability/7. Events and Their Complements.mp4 62.0 MB
  • 52. Deep Learning - Conclusion/4. An overview of CNNs.mp4 61.6 MB
  • 22. Part 4 Introduction to Python/1. Introduction to Programming.mp4 61.4 MB
  • 14. Part 3 Statistics/1. Population and Sample.mp4 60.9 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp4 60.7 MB
  • 35/8. Practical Example Linear Regression (Part 5).mp4 60.7 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp4 60.2 MB
  • 32/1. The Linear Regression Model.mp4 60.2 MB
  • 10. Probability - Combinatorics/11. Solving Combinations.mp4 60.1 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 60.1 MB
  • 11. Probability - Bayesian Inference/7. Union of Sets.mp4 60.0 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4 59.8 MB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 59.3 MB
  • 54/4. MNIST Model Outline.mp4 59.1 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 58.8 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp4 58.8 MB
  • 35/6. Practical Example Linear Regression (Part 4).mp4 58.8 MB
  • 20. Statistics - Hypothesis Testing/10. p-value.mp4 58.6 MB
  • 12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.mp4 58.5 MB
  • 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 58.3 MB
  • 15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 57.0 MB
  • 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 57.0 MB
  • 60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 56.9 MB
  • 20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 56.9 MB
  • 2/3. What is the difference between Analysis and Analytics.mp4 56.2 MB
  • 11. Probability - Bayesian Inference/1. Sets and Events.mp4 56.1 MB
  • 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 56.0 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp4 55.7 MB
  • 59/5. Splitting the Data for Training and Testing.mp4 55.3 MB
  • 59/8. Interpreting the Coefficients for Our Problem.mp4 54.9 MB
  • 57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 54.8 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp4 54.7 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 54.3 MB
  • 22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4 53.5 MB
  • 49. Deep Learning - Preprocessing/3. Standardization.mp4 53.5 MB
  • 15. Statistics - Descriptive Statistics/22. Variance.mp4 53.4 MB
  • 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 52.8 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 52.4 MB
  • 11. Probability - Bayesian Inference/20. Bayes' Law.mp4 52.4 MB
  • 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 52.3 MB
  • 51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp4 52.2 MB
  • 40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4 52.2 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp4 52.1 MB
  • 32/13. Decomposition of Variability.mp4 52.1 MB
  • 40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 51.8 MB
  • 59/12. Testing the Model We Created.mp4 51.4 MB
  • 1/1. A Practical Example What You Will Learn in This Course.mp4 51.4 MB
  • 11. Probability - Bayesian Inference/18. The Multiplication Law.mp4 51.4 MB
  • 12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.mp4 50.6 MB
  • 12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4 50.2 MB
  • 17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 50.1 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 50.1 MB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp4 50.0 MB
  • 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp4 49.7 MB
  • 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp4 49.3 MB
  • 54/8. MNIST Learning.mp4 49.0 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp4 48.2 MB
  • 35/2. Practical Example Linear Regression (Part 2).mp4 48.2 MB
  • 11. Probability - Bayesian Inference/13. The Conditional Probability Formula.mp4 48.1 MB
  • 59/2. Creating the Targets for the Logistic Regression.mp4 48.0 MB
  • 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 47.3 MB
  • 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 47.3 MB
  • 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 46.9 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp4 46.8 MB
  • 32/11. How to Interpret the Regression Table.mp4 46.8 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 46.7 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp4 46.7 MB
  • 32/8. First Regression in Python.mp4 46.7 MB
  • 59/16. Preparing the Deployment of the Model through a Module.mp4 46.6 MB
  • 22. Part 4 Introduction to Python/5. Why Jupyter.mp4 46.5 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 46.3 MB
  • 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 46.1 MB
  • 54/6. Calculating the Accuracy of the Model.mp4 46.0 MB
  • 10. Probability - Combinatorics/9. Solving Variations without Repetition.mp4 45.2 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 45.1 MB
  • 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 45.0 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 44.9 MB
  • 10. Probability - Combinatorics/3. Permutations and How to Use Them.mp4 44.8 MB
  • 59/6. Fitting the Model and Assessing its Accuracy.mp4 43.6 MB
  • 50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp4 43.5 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp4 43.5 MB
  • 10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 43.3 MB
  • 59/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 43.2 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp4 43.0 MB
  • 32/17. R-Squared.mp4 43.0 MB
  • 50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4 43.0 MB
  • 57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 42.8 MB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 42.6 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp4 42.6 MB
  • 32/7. Python Packages Installation.mp4 42.6 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 42.5 MB
  • 59/10. Interpreting the Coefficients of the Logistic Regression.mp4 42.4 MB
  • 10. Probability - Combinatorics/13. Symmetry of Combinations.mp4 42.3 MB
  • 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 42.2 MB
  • 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp4 42.2 MB
  • 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 41.7 MB
  • 52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 41.7 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 41.5 MB
  • 59/11. Backward Elimination or How to Simplify Your Model.mp4 41.5 MB
  • 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 41.3 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp4 41.3 MB
  • 57. Case Study - What's Next in the Course/2. The Business Task.mp4 41.1 MB
  • 59/7. Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 40.6 MB
  • 10. Probability - Combinatorics/19. A Recap of Combinatorics.mp4 40.4 MB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 40.4 MB
  • 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 40.3 MB
  • 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 40.3 MB
  • 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 40.2 MB
  • 40. Part 6 Mathematics/13. Transpose of a Matrix.mp4 39.9 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 39.5 MB
  • 59/13. Saving the Model and Preparing it for Deployment.mp4 39.3 MB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp4 39.2 MB
  • 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 39.0 MB
  • 15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 38.9 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 38.6 MB
  • 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4 38.2 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4 38.1 MB
  • 37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 37.9 MB
  • 10. Probability - Combinatorics/5. Simple Operations with Factorials.mp4 37.9 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4 37.2 MB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 36.6 MB
  • 45/7. Backpropagation.mp4 36.6 MB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 36.6 MB
  • 11. Probability - Bayesian Inference/15. The Law of Total Probability.mp4 36.6 MB
  • 11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp4 36.5 MB
  • 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 36.4 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.mp4 36.4 MB
  • 12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4 35.8 MB
  • 10. Probability - Combinatorics/7. Solving Variations with Repetition.mp4 35.7 MB
  • 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 35.6 MB
  • 40. Part 6 Mathematics/3. Scalars and Vectors.mp4 35.5 MB
  • 30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 35.2 MB
  • 40. Part 6 Mathematics/1. What is a matrix.mp4 35.2 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.mp4 35.1 MB
  • 26. Python - Conditional Statements/4. The ELIF Statement.mp4 34.8 MB
  • 10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 34.8 MB
  • 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 34.5 MB
  • 46. Deep Learning - Overfitting/3. What is Validation.mp4 34.3 MB
  • 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4 34.2 MB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp4 34.1 MB
  • 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 33.8 MB
  • 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 33.8 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4 33.8 MB
  • 51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp4 32.7 MB
  • 41. Part 7 Deep Learning/1. What to Expect from this Part.mp4 32.6 MB
  • 46. Deep Learning - Overfitting/1. What is Overfitting.mp4 32.6 MB
  • 28. Python - Sequences/5. List Slicing.mp4 32.3 MB
  • 23. Python - Variables and Data Types/5. Python Strings.mp4 32.3 MB
  • 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 32.1 MB
  • 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 32.0 MB
  • 51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp4 31.9 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.mp4 31.7 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 31.6 MB
  • 25. Python - Other Python Operators/3. Logical and Identity Operators.mp4 31.5 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 31.4 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 31.1 MB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 31.0 MB
  • 45/2. What is a Deep Net.mp4 31.0 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 31.0 MB
  • 50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4 31.0 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 30.9 MB
  • 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 30.8 MB
  • 48/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 30.5 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 30.5 MB
  • 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 30.5 MB
  • 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 30.3 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4 30.2 MB
  • 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 30.1 MB
  • 48/1. Stochastic Gradient Descent.mp4 30.1 MB
  • 42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 29.8 MB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp4 29.7 MB
  • 32/15. What is the OLS.mp4 29.7 MB
  • 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp4 29.6 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 29.3 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 29.2 MB
  • 49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 29.1 MB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 29.0 MB
  • 45/4. Non-Linearities and their Purpose.mp4 29.0 MB
  • 59/1. Exploring the Problem with a Machine Learning Mindset.mp4 28.9 MB
  • 15. Statistics - Descriptive Statistics/27. Covariance.mp4 28.8 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 28.6 MB
  • 12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.mp4 28.5 MB
  • 36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 28.4 MB
  • 11. Probability - Bayesian Inference/5. Intersection of Sets.mp4 28.3 MB
  • 11. Probability - Bayesian Inference/16. The Additive Rule.mp4 28.3 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4 28.1 MB
  • 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 28.0 MB
  • 48/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 27.6 MB
  • 12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4 27.6 MB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 27.2 MB
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  • 54/5. MNIST Loss and Optimization Algorithm.mp4 27.1 MB
  • 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 27.1 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp4 27.0 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 26.9 MB
  • 60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 26.7 MB
  • 11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.mp4 26.6 MB
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  • 52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 26.5 MB
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  • 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 25.3 MB
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  • 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 24.2 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.mp4 24.0 MB
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  • 52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 21.1 MB
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  • 15. Statistics - Descriptive Statistics/24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx 11.2 kB
  • 20. Statistics - Hypothesis Testing/8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx 11.2 kB
  • 51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.vtt 11.2 kB
  • 15. Statistics - Descriptive Statistics/18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx 11.1 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx 11.1 kB
  • 15. Statistics - Descriptive Statistics/23.1 2.9. Variance_exercise.xlsx.xlsx 11.1 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/8.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx 11.0 kB
  • 20. Statistics - Hypothesis Testing/17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx 11.0 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/9.2 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx 10.9 kB
  • 20. Statistics - Hypothesis Testing/20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx 10.8 kB
  • 15. Statistics - Descriptive Statistics/17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx 10.7 kB
  • 2/7. Continuing with BI, ML, and AI.vtt 10.7 kB
  • 17. Statistics - Inferential Statistics Fundamentals/6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx 10.6 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/5.1 Categorical.csv.csv 10.6 kB
  • 40. Part 6 Mathematics/16. Why is Linear Algebra Useful.vtt 10.6 kB
  • 15. Statistics - Descriptive Statistics/22.1 2.9. Variance_lesson.xlsx.xlsx 10.3 kB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).vtt 10.3 kB
  • 35/6. Practical Example Linear Regression (Part 4).vtt 10.3 kB
  • 20. Statistics - Hypothesis Testing/14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx 10.0 kB
  • 12. Probability - Distributions/29.6 Customers_Membership.xlsx.xlsx 9.9 kB
  • 20. Statistics - Hypothesis Testing/16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx 9.9 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.vtt 9.8 kB
  • 12. Probability - Distributions/29.3 Daily Views.xlsx.xlsx 9.8 kB
  • 15. Statistics - Descriptive Statistics/21.2 2.8. Skewness_exercise.xlsx.xlsx 9.7 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).vtt 9.7 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting acquainted with the dataset.vtt 9.6 kB
  • 20. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx 9.5 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.vtt 9.5 kB
  • 51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.vtt 9.5 kB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).vtt 9.5 kB
  • 35/8. Practical Example Linear Regression (Part 5).vtt 9.5 kB
  • 2/5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt 9.5 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.vtt 9.5 kB
  • 56. Software Integration/5. Taking a Closer Look at APIs.vtt 9.4 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.vtt 9.2 kB
  • 54/8. MNIST Learning.vtt 9.1 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.vtt 9.0 kB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.vtt 9.0 kB
  • 13. Probability - Probability in Other Fields/1. Probability in Finance.vtt 8.9 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.vtt 8.9 kB
  • 12. Probability - Distributions/3. Types of Probability Distributions.vtt 8.6 kB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.vtt 8.6 kB
  • 36. Advanced Statistical Methods - Logistic Regression/16.1 Bank_data_testing.csv.csv 8.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.vtt 8.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/3.2 Countries_exercise.csv.csv 8.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/7.2 Countries_exercise.csv.csv 8.5 kB
  • 40. Part 6 Mathematics/15. Dot Product of Matrices.vtt 8.4 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.vtt 8.3 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).vtt 8.1 kB
  • 54/4. MNIST Model Outline.vtt 8.1 kB
  • 3/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 8.1 kB
  • 9. Part 2 Probability/1. The Basic Probability Formula.vtt 8.0 kB
  • 22. Part 4 Introduction to Python/7. Installing Python and Jupyter.vtt 8.0 kB
  • 20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.vtt 7.9 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.vtt 7.9 kB
  • 12. Probability - Distributions/15. Characteristics of Continuous Distributions.vtt 7.8 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.vtt 7.7 kB
  • 56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.vtt 7.7 kB
  • 13. Probability - Probability in Other Fields/2. Probability in Statistics.vtt 7.7 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.vtt 7.6 kB
  • 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.vtt 7.6 kB
  • 21/1. Practical Example Hypothesis Testing.vtt 7.6 kB
  • 12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.vtt 7.6 kB
  • 59/2. Creating the Targets for the Logistic Regression.vtt 7.5 kB
  • 54/9. MNIST Results and Testing.vtt 7.3 kB
  • 20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.vtt 7.3 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.vtt 7.3 kB
  • 59/5. Splitting the Data for Training and Testing.vtt 7.2 kB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).vtt 7.2 kB
  • 35/2. Practical Example Linear Regression (Part 2).vtt 7.2 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.vtt 7.1 kB
  • 59/8. Interpreting the Coefficients for Our Problem.vtt 7.1 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.vtt 7.1 kB
  • 32/8. First Regression in Python.vtt 7.1 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.vtt 7.1 kB
  • 51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.vtt 7.1 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.vtt 7.0 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.vtt 7.0 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.vtt 7.0 kB
  • 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.vtt 7.0 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.vtt 7.0 kB
  • 12. Probability - Distributions/1. Fundamentals of Probability Distributions.vtt 6.9 kB
  • 15. Statistics - Descriptive Statistics/22. Variance.vtt 6.8 kB
  • 60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.vtt 6.8 kB
  • 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.vtt 6.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).vtt 6.7 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 6.6 kB
  • 23. Python - Variables and Data Types/5. Python Strings.vtt 6.6 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.vtt 6.6 kB
  • 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.vtt 6.6 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.vtt 6.6 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.vtt 6.6 kB
  • 59/6. Fitting the Model and Assessing its Accuracy.vtt 6.6 kB
  • 11. Probability - Bayesian Inference/20. Bayes' Law.vtt 6.6 kB
  • 59/10. Interpreting the Coefficients of the Logistic Regression.vtt 6.5 kB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.vtt 6.5 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.vtt 6.4 kB
  • 36. Advanced Statistical Methods - Logistic Regression/5.2 Example_bank_data.csv.csv 6.4 kB
  • 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.vtt 6.3 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.vtt 6.3 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.vtt 6.3 kB
  • 32/1. The Linear Regression Model.vtt 6.3 kB
  • 22. Part 4 Introduction to Python/3. Why Python.vtt 6.3 kB
  • 22. Part 4 Introduction to Python/1. Introduction to Programming.vtt 6.2 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.vtt 6.2 kB
  • 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.vtt 6.2 kB
  • 9. Part 2 Probability/7. Events and Their Complements.vtt 6.1 kB
  • 56. Software Integration/9. Software Integration - Explained.vtt 6.1 kB
  • 9. Part 2 Probability/3. Computing Expected Values.vtt 6.0 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).vtt 6.0 kB
  • 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.vtt 6.0 kB
  • 13. Probability - Probability in Other Fields/3. Probability in Data Science.vtt 6.0 kB
  • 2/1. Data Science and Business Buzzwords Why are there so many.vtt 6.0 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.vtt 6.0 kB
  • 45/3. Digging into a Deep Net.vtt 6.0 kB
  • 12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.vtt 5.9 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.vtt 5.9 kB
  • 32/17. R-Squared.vtt 5.9 kB
  • 59/7. Creating a Summary Table with the Coefficients and Intercept.vtt 5.9 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.vtt 5.9 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.vtt 5.9 kB
  • 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.vtt 5.9 kB
  • 26. Python - Conditional Statements/4. The ELIF Statement.vtt 5.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.vtt 5.8 kB
  • 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.vtt 5.8 kB
  • 59/12. Testing the Model We Created.vtt 5.8 kB
  • 9. Part 2 Probability/5. Frequency.vtt 5.8 kB
  • 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.vtt 5.8 kB
  • 52. Deep Learning - Conclusion/4. An overview of CNNs.vtt 5.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.vtt 5.8 kB
  • 1/1. A Practical Example What You Will Learn in This Course.vtt 5.8 kB
  • 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.vtt 5.7 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.vtt 5.7 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.vtt 5.6 kB
  • 32/11. How to Interpret the Regression Table.vtt 5.6 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.vtt 5.6 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.vtt 5.6 kB
  • 51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.vtt 5.6 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.vtt 5.6 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.vtt 5.5 kB
  • 30. Python - Advanced Python Tools/1. Object Oriented Programming.vtt 5.5 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).vtt 5.5 kB
  • 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.vtt 5.4 kB
  • 49. Deep Learning - Preprocessing/3. Standardization.vtt 5.4 kB
  • 23. Python - Variables and Data Types/1. Variables.vtt 5.4 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.vtt 5.4 kB
  • 15. Statistics - Descriptive Statistics/1. Types of Data.vtt 5.4 kB
  • 48/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt 5.3 kB
  • 56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.vtt 5.3 kB
  • 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.vtt 5.3 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.vtt 5.3 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.vtt 5.2 kB
  • 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.vtt 5.2 kB
  • 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.vtt 5.2 kB
  • 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.vtt 5.2 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.vtt 5.1 kB
  • 15. Statistics - Descriptive Statistics/17. Mean, median and mode.vtt 5.1 kB
  • 25. Python - Other Python Operators/3. Logical and Identity Operators.vtt 5.1 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt 5.1 kB
  • 10. Probability - Combinatorics/11. Solving Combinations.vtt 5.1 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.vtt 5.1 kB
  • 11. Probability - Bayesian Inference/7. Union of Sets.vtt 5.1 kB
  • 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.vtt 5.1 kB
  • 46. Deep Learning - Overfitting/1. What is Overfitting.vtt 5.1 kB
  • 59/16. Preparing the Deployment of the Model through a Module.vtt 5.0 kB
  • 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.vtt 5.0 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.vtt 5.0 kB
  • 32/7. Python Packages Installation.vtt 5.0 kB
  • 59/13. Saving the Model and Preparing it for Deployment.vtt 5.0 kB
  • 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.vtt 5.0 kB
  • 28. Python - Sequences/5. List Slicing.vtt 4.9 kB
  • 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).vtt 4.9 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.vtt 4.9 kB
  • 14. Part 3 Statistics/1. Population and Sample.vtt 4.9 kB
  • 56. Software Integration/7. Communication between Software Products through Text Files.vtt 4.9 kB
  • 57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 4.9 kB
  • 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.vtt 4.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.vtt 4.9 kB
  • 12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.vtt 4.8 kB
  • 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.vtt 4.8 kB
  • 40. Part 6 Mathematics/13. Transpose of a Matrix.vtt 4.8 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.vtt 4.8 kB
  • 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.vtt 4.7 kB
  • 52. Deep Learning - Conclusion/1. Summary on What You've Learned.vtt 4.7 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.vtt 4.7 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.vtt 4.7 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.vtt 4.7 kB
  • 45/5. Activation Functions.vtt 4.7 kB
  • 59/11. Backward Elimination or How to Simplify Your Model.vtt 4.7 kB
  • 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.vtt 4.7 kB
  • 48/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt 4.7 kB
  • 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.vtt 4.7 kB
  • 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).vtt 4.7 kB
  • 54/6. Calculating the Accuracy of the Model.vtt 4.6 kB
  • 1/2. What Does the Course Cover.vtt 4.6 kB
  • 11. Probability - Bayesian Inference/1. Sets and Events.vtt 4.6 kB
  • 20. Statistics - Hypothesis Testing/10. p-value.vtt 4.6 kB
  • 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.vtt 4.6 kB
  • 2/9. A Breakdown of our Data Science Infographic.vtt 4.6 kB
  • 11. Probability - Bayesian Inference/13. The Conditional Probability Formula.vtt 4.5 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.vtt 4.5 kB
  • 2/3. What is the difference between Analysis and Analytics.vtt 4.5 kB
  • 59/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt 4.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.vtt 4.5 kB
  • 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.vtt 4.4 kB
  • 15. Statistics - Descriptive Statistics/27. Covariance.vtt 4.4 kB
  • 28. Python - Sequences/1. Lists.vtt 4.4 kB
  • 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.vtt 4.4 kB
  • 46. Deep Learning - Overfitting/3. What is Validation.vtt 4.4 kB
  • 12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.vtt 4.3 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.vtt 4.3 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.vtt 4.3 kB
  • 60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.vtt 4.3 kB
  • 48/1. Stochastic Gradient Descent.vtt 4.3 kB
  • 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.vtt 4.3 kB
  • 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.vtt 4.3 kB
  • 30. Python - Advanced Python Tools/7. Importing Modules in Python.vtt 4.3 kB
  • 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.vtt 4.2 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.vtt 4.2 kB
  • 51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.vtt 4.2 kB
  • 22. Part 4 Introduction to Python/5. Why Jupyter.vtt 4.2 kB
  • 11. Probability - Bayesian Inference/18. The Multiplication Law.vtt 4.2 kB
  • 41. Part 7 Deep Learning/1. What to Expect from this Part.vtt 4.1 kB
  • 15. Statistics - Descriptive Statistics/3. Levels of Measurement.vtt 4.1 kB
  • 59/1. Exploring the Problem with a Machine Learning Mindset.vtt 4.1 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.vtt 4.1 kB
  • 10. Probability - Combinatorics/9. Solving Variations without Repetition.vtt 4.1 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).vtt 4.1 kB
  • 51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.vtt 4.1 kB
  • 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.vtt 4.0 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.vtt 4.0 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.vtt 4.0 kB
  • 45/7. Backpropagation.vtt 4.0 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).vtt 4.0 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.vtt 4.0 kB
  • 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.vtt 4.0 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.vtt 4.0 kB
  • 45/6. Activation Functions Softmax Activation.vtt 4.0 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).vtt 4.0 kB
  • 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.vtt 3.9 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.vtt 3.9 kB
  • 40. Part 6 Mathematics/1. What is a matrix.vtt 3.9 kB
  • 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.vtt 3.9 kB
  • 10. Probability - Combinatorics/13. Symmetry of Combinations.vtt 3.9 kB
  • 27. Python - Python Functions/2. How to Create a Function with a Parameter.vtt 3.9 kB
  • 40. Part 6 Mathematics/14. Dot Product.vtt 3.8 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.vtt 3.8 kB
  • 59/4. Standardizing the Data.vtt 3.8 kB
  • 27. Python - Python Functions/7. Built-in Functions in Python.vtt 3.8 kB
  • 46. Deep Learning - Overfitting/5. N-Fold Cross Validation.vtt 3.8 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.vtt 3.8 kB
  • 32/13. Decomposition of Variability.vtt 3.8 kB
  • 57. Case Study - What's Next in the Course/3. Introducing the Data Set.vtt 3.7 kB
  • 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.vtt 3.7 kB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).vtt 3.7 kB
  • 35/4. Practical Example Linear Regression (Part 3).vtt 3.7 kB
  • 28. Python - Sequences/7. Dictionaries.vtt 3.7 kB
  • 10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.vtt 3.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/15.3 iris_with_answers.csv.csv 3.7 kB
  • 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.vtt 3.7 kB
  • 10. Probability - Combinatorics/3. Permutations and How to Use Them.vtt 3.7 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.vtt 3.7 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.vtt 3.7 kB
  • 24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.vtt 3.7 kB
  • 40. Part 6 Mathematics/5. Linear Algebra and Geometry.vtt 3.6 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.vtt 3.6 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.vtt 3.6 kB
  • 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.vtt 3.6 kB
  • 12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.vtt 3.6 kB
  • 28. Python - Sequences/3. Using Methods.vtt 3.6 kB
  • 17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.vtt 3.5 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.vtt 3.5 kB
  • 45/8. Backpropagation picture.vtt 3.5 kB
  • 42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).vtt 3.5 kB
  • 49. Deep Learning - Preprocessing/1. Preprocessing Introduction.vtt 3.5 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.vtt 3.5 kB
  • 45/4. Non-Linearities and their Purpose.vtt 3.5 kB
  • 29. Python - Iterations/8. How to Iterate over Dictionaries.vtt 3.4 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.vtt 3.4 kB
  • 32/15. What is the OLS.vtt 3.4 kB
  • 10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.vtt 3.4 kB
  • 52. Deep Learning - Conclusion/5. An Overview of RNNs.vtt 3.4 kB
  • 57. Case Study - What's Next in the Course/2. The Business Task.vtt 3.4 kB
  • 40. Part 6 Mathematics/3. Scalars and Vectors.vtt 3.4 kB
  • 17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.vtt 3.4 kB
  • 10. Probability - Combinatorics/19. A Recap of Combinatorics.vtt 3.3 kB
  • 22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt 3.3 kB
  • 47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt 3.3 kB
  • 47. Deep Learning - Initialization/2. Types of Simple Initializations.vtt 3.3 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.vtt 3.3 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.vtt 3.3 kB
  • 15. Statistics - Descriptive Statistics/19. Skewness.vtt 3.3 kB
  • 23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.vtt 3.2 kB
  • 40. Part 6 Mathematics/8. What is a Tensor.vtt 3.2 kB
  • 54/2. MNIST How to Tackle the MNIST.vtt 3.2 kB
  • 30. Python - Advanced Python Tools/5. What is the Standard Library.vtt 3.2 kB
  • 29. Python - Iterations/6. Conditional Statements and Loops.vtt 3.2 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.vtt 3.2 kB
  • 59/3. Selecting the Inputs for the Logistic Regression.vtt 3.2 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.vtt 3.2 kB
  • 11. Probability - Bayesian Inference/15. The Law of Total Probability.vtt 3.2 kB
  • 26. Python - Conditional Statements/1. The IF Statement.vtt 3.2 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.vtt 3.2 kB
  • 46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.vtt 3.2 kB
  • 54/5. MNIST Loss and Optimization Algorithm.vtt 3.2 kB
  • 10. Probability - Combinatorics/7. Solving Variations with Repetition.vtt 3.2 kB
  • 47. Deep Learning - Initialization/1. What is Initialization.vtt 3.2 kB
  • 54/1. MNIST What is the MNIST Dataset.vtt 3.1 kB
  • 11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.vtt 3.1 kB
  • 27. Python - Python Functions/5. Conditional Statements and Functions.vtt 3.1 kB
  • 48/3. Momentum.vtt 3.1 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.vtt 3.1 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.vtt 3.1 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.vtt 3.0 kB
  • 28. Python - Sequences/6. Tuples.vtt 3.0 kB
  • 22. Part 4 Introduction to Python/11. Python 2 vs Python 3.vtt 3.0 kB
  • 48/7. Adam (Adaptive Moment Estimation).vtt 3.0 kB
  • 10. Probability - Combinatorics/5. Simple Operations with Factorials.vtt 3.0 kB
  • 36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.vtt 3.0 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.vtt 3.0 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.vtt 2.9 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/5. What's Regression Analysis - a Quick Refresher.html 2.9 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.vtt 2.9 kB
  • 45/2. What is a Deep Net.vtt 2.9 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.vtt 2.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.vtt 2.8 kB
  • 42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.vtt 2.8 kB
  • 27. Python - Python Functions/3. Defining a Function in Python - Part II.vtt 2.8 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.vtt 2.7 kB
  • 15. Statistics - Descriptive Statistics/11. The Histogram.vtt 2.7 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.vtt 2.7 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.vtt 2.7 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).vtt 2.6 kB
  • 54/7. MNIST Batching and Early Stopping.vtt 2.6 kB
  • 26. Python - Conditional Statements/5. A Note on Boolean Values.vtt 2.6 kB
  • 12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.vtt 2.6 kB
  • 48/2. Problems with Gradient Descent.vtt 2.6 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/1. What to Expect from the Following Sections.html 2.5 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.vtt 2.5 kB
  • 12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.vtt 2.5 kB
  • 26. Python - Conditional Statements/3. The ELSE Statement.vtt 2.5 kB
  • 29. Python - Iterations/4. Lists with the range() Function.vtt 2.5 kB
  • 29. Python - Iterations/1. For Loops.vtt 2.5 kB
  • 42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.vtt 2.5 kB
  • 11. Probability - Bayesian Inference/16. The Additive Rule.vtt 2.5 kB
  • 12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.vtt 2.5 kB
  • 49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.vtt 2.5 kB
  • 29. Python - Iterations/3. While Loops and Incrementing.vtt 2.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/14.1 iris_dataset.csv.csv 2.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/15.1 iris_dataset.csv.csv 2.5 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.vtt 2.4 kB
  • 42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.vtt 2.4 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/14. Dropping a Dummy Variable from the Data Set.html 2.4 kB
  • 46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.vtt 2.4 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/3. A Note on Installing Packages in Anaconda.html 2.4 kB
  • 20. Statistics - Hypothesis Testing/2. Further Reading on Null and Alternative Hypothesis.html 2.3 kB
  • 40. Part 6 Mathematics/12. Errors when Adding Matrices.vtt 2.3 kB
  • 52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.vtt 2.3 kB
  • 11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.vtt 2.3 kB
  • 27. Python - Python Functions/1. Defining a Function in Python.vtt 2.3 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.vtt 2.2 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.vtt 2.2 kB
  • 11. Probability - Bayesian Inference/5. Intersection of Sets.vtt 2.2 kB
  • 54/10. MNIST Solutions.html 2.2 kB
  • 12. Probability - Distributions/5. Characteristics of Discrete Distributions.vtt 2.2 kB
  • 59/14. ARTICLE - A Note on 'pickling'.html 2.2 kB
  • 25. Python - Other Python Operators/1. Comparison Operators.vtt 2.2 kB
  • 54/11. MNIST Exercises.html 2.2 kB
  • 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.vtt 2.2 kB
  • 45/1. What is a Layer.vtt 2.2 kB
  • 29. Python - Iterations/7. Conditional Statements, Functions, and Loops.vtt 2.1 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/11. MNIST - Exercises.html 2.0 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.vtt 2.0 kB
  • 24. Python - Basic Python Syntax/12. Structuring with Indentation.vtt 2.0 kB
  • 31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.vtt 2.0 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.vtt 2.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.vtt 2.0 kB
  • 48/5. Learning Rate Schedules Visualized.vtt 1.9 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).vtt 1.9 kB
  • 54/3. MNIST Relevant Packages.vtt 1.9 kB
  • 42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.vtt 1.9 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.vtt 1.9 kB
  • 32/3. Correlation vs Regression.vtt 1.9 kB
  • 51. Deep Learning - Business Case Example/11. Business Case Testing the Model.vtt 1.8 kB
  • 27. Python - Python Functions/4. How to Use a Function within a Function.vtt 1.8 kB
  • 17. Statistics - Inferential Statistics Fundamentals/11. Standard error.vtt 1.8 kB
  • 51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.vtt 1.8 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).vtt 1.8 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5. Basic NN Example Exercises.html 1.7 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.vtt 1.6 kB
  • 24. Python - Basic Python Syntax/3. The Double Equality Sign.vtt 1.6 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10. Basic NN Example with TF Exercises.html 1.6 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.vtt 1.5 kB
  • 24. Python - Basic Python Syntax/7. Add Comments.vtt 1.5 kB
  • 24. Python - Basic Python Syntax/10. Indexing Elements.vtt 1.5 kB
  • 49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.vtt 1.5 kB
  • 32/5. Geometrical Representation of the Linear Regression Model.vtt 1.5 kB
  • 17. Statistics - Inferential Statistics Fundamentals/1. Introduction.vtt 1.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.vtt 1.5 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/9. First Regression in Python Exercise.html 1.4 kB
  • 32/9. First Regression in Python Exercise.html 1.4 kB
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.vtt 1.3 kB
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  • 44. Deep Learning - TensorFlow 2.0 Introduction/9. Basic NN with TensorFlow Exercises.html 1.3 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.vtt 1.2 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/29. EXERCISE - Removing the Date Column.html 1.2 kB
  • 10. Probability - Combinatorics/1. Fundamentals of Combinatorics.vtt 1.2 kB
  • 24. Python - Basic Python Syntax/5. How to Reassign Values.vtt 1.2 kB
  • 30. Python - Advanced Python Tools/3. Modules and Packages.vtt 1.2 kB
  • 27. Python - Python Functions/6. Functions Containing a Few Arguments.vtt 1.2 kB
  • 52. Deep Learning - Conclusion/3. DeepMind and Deep Learning.html 1.1 kB
  • 24. Python - Basic Python Syntax/9. Understanding Line Continuation.vtt 1.0 kB
  • 60. Case Study - Loading the 'absenteeism_module'/4. Exporting the Obtained Data Set as a .csv.html 998 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/8. EXERCISE - Dropping a Column from a DataFrame in Python.html 866 Bytes
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/3. A Note on Multicollinearity.html 849 Bytes
  • 35/3. A Note on Multicollinearity.html 849 Bytes
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/7. Dummy Variables - Exercise.html 713 Bytes
  • 35/7. Dummy Variables - Exercise.html 713 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/1. READ ME!!!!.html 564 Bytes
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/5. EXERCISE - Transportation Expense vs Probability.html 553 Bytes
  • 45/9. Backpropagation - A Peek into the Mathematics of Optimization.html 539 Bytes
  • 15. Statistics - Descriptive Statistics/23. Variance Exercise.html 522 Bytes
  • 60. Case Study - Loading the 'absenteeism_module'/1. Are You Sure You're All Set.html 519 Bytes
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/9. Linear Regression - Exercise.html 503 Bytes
  • 35/9. Linear Regression - Exercise.html 503 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 471 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/12. Business Case Final Exercise.html 439 Bytes
  • 51. Deep Learning - Business Case Example/12. Business Case Final Exercise.html 433 Bytes
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/3. EXERCISE - Reasons vs Probability.html 397 Bytes
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/1. EXERCISE - Age vs Probability.html 385 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/5. Business Case Preprocessing Exercise.html 383 Bytes
  • 51. Deep Learning - Business Case Example/5. Business Case Preprocessing the Data - Exercise.html 370 Bytes
  • 59/15. EXERCISE - Saving the Model (and Scaler).html 284 Bytes
  • 59/11.1 Logistic Regression prior to Backward Elimination.html 226 Bytes
  • 40. Part 6 Mathematics/12.1 Errors when Adding Matrices Python Notebook.html 220 Bytes
  • 59/9.1 Logistic Regression prior to Custom Scaler.html 219 Bytes
  • 59/15.2 Logistic Regression with Comments.html 210 Bytes
  • 59/15.1 Logistic Regression.html 196 Bytes
  • 51. Deep Learning - Business Case Example/10. Setting an Early Stopping Mechanism - Exercise.html 192 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/18. EXERCISE - Using .concat() in Python.html 189 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/29.2 Removing the “Date” Column.html 188 Bytes
  • 60. Case Study - Loading the 'absenteeism_module'/4.1 Deploying the ‘absenteeism_module.html 185 Bytes
  • 40. Part 6 Mathematics/7.1 Arrays in Python Notebook.html 181 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/23.1 Creating Checkpoints.html 181 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/29.1 Preprocessing.html 181 Bytes
  • 40. Part 6 Mathematics/10.1 Addition and Subtraction of Matrices Python Notebook.html 178 Bytes
  • 54/10.7 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html 172 Bytes
  • 54/10.9 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html 172 Bytes
  • 40. Part 6 Mathematics/15.1 Dot Product of Matrices Python Notebook.html 171 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/32.1 Exercises and solutions.html 170 Bytes
  • 40. Part 6 Mathematics/13.1 Transpose of a Matrix Python Notebook.html 167 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167 Bytes
  • 10. Probability - Combinatorics/10. Solving Variations without Repetition.html 165 Bytes
  • 10. Probability - Combinatorics/12. Solving Combinations.html 165 Bytes
  • 10. Probability - Combinatorics/14. Symmetry of Combinations.html 165 Bytes
  • 10. Probability - Combinatorics/16. Solving Combinations with Separate Sample Spaces.html 165 Bytes
  • 10. Probability - Combinatorics/18. Combinatorics in Real-Life The Lottery.html 165 Bytes
  • 10. Probability - Combinatorics/2. Fundamentals of Combinatorics.html 165 Bytes
  • 10. Probability - Combinatorics/4. Permutations and How to Use Them.html 165 Bytes
  • 10. Probability - Combinatorics/6. Simple Operations with Factorials.html 165 Bytes
  • 10. Probability - Combinatorics/8. Solving Variations with Repetition.html 165 Bytes
  • 11. Probability - Bayesian Inference/10. Mutually Exclusive Sets.html 165 Bytes
  • 11. Probability - Bayesian Inference/12. Dependence and Independence of Sets.html 165 Bytes
  • 11. Probability - Bayesian Inference/14. The Conditional Probability Formula.html 165 Bytes
  • 11. Probability - Bayesian Inference/17. The Additive Rule.html 165 Bytes
  • 11. Probability - Bayesian Inference/19. The Multiplication Law.html 165 Bytes
  • 11. Probability - Bayesian Inference/2. Sets and Events.html 165 Bytes
  • 11. Probability - Bayesian Inference/21. Bayes' Law.html 165 Bytes
  • 11. Probability - Bayesian Inference/4. Ways Sets Can Interact.html 165 Bytes
  • 11. Probability - Bayesian Inference/6. Intersection of Sets.html 165 Bytes
  • 11. Probability - Bayesian Inference/8. Union of Sets.html 165 Bytes
  • 12. Probability - Distributions/10. Discrete Distributions The Bernoulli Distribution.html 165 Bytes
  • 12. Probability - Distributions/12. Discrete Distributions The Binomial Distribution.html 165 Bytes
  • 12. Probability - Distributions/14. Discrete Distributions The Poisson Distribution.html 165 Bytes
  • 12. Probability - Distributions/16. Characteristics of Continuous Distributions.html 165 Bytes
  • 12. Probability - Distributions/18. Continuous Distributions The Normal Distribution.html 165 Bytes
  • 12. Probability - Distributions/2. Fundamentals of Probability Distributions.html 165 Bytes
  • 12. Probability - Distributions/20. Continuous Distributions The Standard Normal Distribution.html 165 Bytes
  • 12. Probability - Distributions/22. Continuous Distributions The Students' T Distribution.html 165 Bytes
  • 12. Probability - Distributions/24. Continuous Distributions The Chi-Squared Distribution.html 165 Bytes
  • 12. Probability - Distributions/26. Continuous Distributions The Exponential Distribution.html 165 Bytes
  • 12. Probability - Distributions/28. Continuous Distributions The Logistic Distribution.html 165 Bytes
  • 12. Probability - Distributions/4. Types of Probability Distributions.html 165 Bytes
  • 12. Probability - Distributions/6. Characteristics of Discrete Distributions.html 165 Bytes
  • 12. Probability - Distributions/8. Discrete Distributions The Uniform Distribution.html 165 Bytes
  • 14. Part 3 Statistics/2. Population and Sample.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/12. The Histogram.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/15. Cross Tables and Scatter Plots.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/2. Types of Data.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/20. Skewness.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/25. Standard Deviation.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/28. Covariance.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/31. Correlation.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/4. Levels of Measurement.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html 165 Bytes
  • 15. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html 165 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html 165 Bytes
  • 18. Statistics - Inferential Statistics Confidence Intervals/11. Margin of Error.html 165 Bytes
  • 18. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html 165 Bytes
  • 18. Statistics - Inferential Statistics Confidence Intervals/7. Student's T Distribution.html 165 Bytes
  • 2/10. A Breakdown of our Data Science Infographic.html 165 Bytes
  • 2/2. Data Science and Business Buzzwords Why are there so many.html 165 Bytes
  • 2/4. What is the difference between Analysis and Analytics.html 165 Bytes
  • 2/6. Business Analytics, Data Analytics, and Data Science An Introduction.html 165 Bytes
  • 2/8. Continuing with BI, ML, and AI.html 165 Bytes
  • 20. Statistics - Hypothesis Testing/11. p-value.html 165 Bytes
  • 20. Statistics - Hypothesis Testing/19. Test for the mean. Independent samples (Part 2).html 165 Bytes
  • 20. Statistics - Hypothesis Testing/3. Null vs Alternative Hypothesis.html 165 Bytes
  • 20. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html 165 Bytes
  • 20. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html 165 Bytes
  • 22. Part 4 Introduction to Python/10. Jupyter's Interface.html 165 Bytes
  • 22. Part 4 Introduction to Python/2. Introduction to Programming.html 165 Bytes
  • 22. Part 4 Introduction to Python/4. Why Python.html 165 Bytes
  • 22. Part 4 Introduction to Python/6. Why Jupyter.html 165 Bytes
  • 23. Python - Variables and Data Types/2. Variables.html 165 Bytes
  • 23. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html 165 Bytes
  • 23. Python - Variables and Data Types/6. Python Strings.html 165 Bytes
  • 24. Python - Basic Python Syntax/11. Indexing Elements.html 165 Bytes
  • 24. Python - Basic Python Syntax/13. Structuring with Indentation.html 165 Bytes
  • 24. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html 165 Bytes
  • 24. Python - Basic Python Syntax/4. The Double Equality Sign.html 165 Bytes
  • 24. Python - Basic Python Syntax/6. How to Reassign Values.html 165 Bytes
  • 24. Python - Basic Python Syntax/8. Add Comments.html 165 Bytes
  • 25. Python - Other Python Operators/2. Comparison Operators.html 165 Bytes
  • 25. Python - Other Python Operators/4. Logical and Identity Operators.html 165 Bytes
  • 26. Python - Conditional Statements/2. The IF Statement.html 165 Bytes
  • 26. Python - Conditional Statements/6. A Note on Boolean Values.html 165 Bytes
  • 27. Python - Python Functions/8. Python Functions.html 165 Bytes
  • 28. Python - Sequences/2. Lists.html 165 Bytes
  • 28. Python - Sequences/4. Using Methods.html 165 Bytes
  • 28. Python - Sequences/8. Dictionaries.html 165 Bytes
  • 29. Python - Iterations/2. For Loops.html 165 Bytes
  • 29. Python - Iterations/5. Lists with the range() Function.html 165 Bytes
  • 3/2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html 165 Bytes
  • 30. Python - Advanced Python Tools/2. Object Oriented Programming.html 165 Bytes
  • 30. Python - Advanced Python Tools/4. Modules and Packages.html 165 Bytes
  • 30. Python - Advanced Python Tools/6. What is the Standard Library.html 165 Bytes
  • 30. Python - Advanced Python Tools/8. Importing Modules in Python.html 165 Bytes
  • 31. Part 5 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/12. How to Interpret the Regression Table.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/14. Decomposition of Variability.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/16. What is the OLS.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/18. R-Squared.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/2. The Linear Regression Model.html 165 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/4. Correlation vs Regression.html 165 Bytes
  • 32/12. How to Interpret the Regression Table.html 165 Bytes
  • 32/14. Decomposition of Variability.html 165 Bytes
  • 32/16. What is the OLS.html 165 Bytes
  • 32/18. R-Squared.html 165 Bytes
  • 32/2. The Linear Regression Model.html 165 Bytes
  • 32/4. Correlation vs Regression.html 165 Bytes
  • 32/6. Geometrical Representation of the Linear Regression Model.html 165 Bytes
  • 4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason behind these Disciplines.html 165 Bytes
  • 40. Part 6 Mathematics/11. Addition and Subtraction of Matrices.html 165 Bytes
  • 40. Part 6 Mathematics/2. What is a Matrix.html 165 Bytes
  • 40. Part 6 Mathematics/4. Scalars and Vectors.html 165 Bytes
  • 40. Part 6 Mathematics/6. Linear Algebra and Geometry.html 165 Bytes
  • 40. Part 6 Mathematics/9. What is a Tensor.html 165 Bytes
  • 41. Part 7 Deep Learning/2. What is Machine Learning.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/4. Training the Model.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html 165 Bytes
  • 42. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/5. Techniques for Working with Big Data.html 165 Bytes
  • 5. The Field of Data Science - Popular Data Science Techniques/8. Business Intelligence (BI) Techniques.html 165 Bytes
  • 54/10.1 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html 165 Bytes
  • 54/10.3 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html 165 Bytes
  • 56. Software Integration/10. Software Integration - Explained.html 165 Bytes
  • 56. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html 165 Bytes
  • 56. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html 165 Bytes
  • 56. Software Integration/6. Taking a Closer Look at APIs.html 165 Bytes
  • 56. Software Integration/8. Communication between Software Products through Text Files.html 165 Bytes
  • 57. Case Study - What's Next in the Course/4. Introducing the Data Set.html 165 Bytes
  • 6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html 165 Bytes
  • 7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html 165 Bytes
  • 8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html 165 Bytes
  • 9. Part 2 Probability/2. The Basic Probability Formula.html 165 Bytes
  • 9. Part 2 Probability/4. Computing Expected Values.html 165 Bytes
  • 9. Part 2 Probability/6. Frequency.html 165 Bytes
  • 9. Part 2 Probability/8. Events and Their Complements.html 165 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.1 Basic NN Example with TensorFlow Exercise 2.3 Solution.html 162 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html 162 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.6 Basic NN Example with TensorFlow Exercise 2.4 Solution.html 162 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.7 Basic NN Example with TensorFlow Exercise 2.2 Solution.html 162 Bytes
  • 54/10.11 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html 162 Bytes
  • 54/10.4 TensorFlow MNIST 'Time' Solution.html 162 Bytes
  • 54/10.6 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html 162 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.3 Basic NN Example with TensorFlow Exercise 1 Solution.html 160 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.4 Basic NN Example with TensorFlow Exercise 3 Solution.html 160 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.8 Basic NN Example with TensorFlow Exercise 4 Solution.html 160 Bytes
  • 54/10.2 TensorFlow MNIST '3. Width and Depth' Solution.html 160 Bytes
  • 54/3.1 TensorFlow MNIST Part 1 with Comments.html 159 Bytes
  • 54/4.1 TensorFlow MNIST Part 2 with Comments.html 159 Bytes
  • 54/5.1 TensorFlow MNIST Part 3 with Comments.html 159 Bytes
  • 54/6.1 TensorFlow MNIST Part 4 with Comments.html 159 Bytes
  • 54/7.1 TensorFlow MNIST Part 5 with Comments.html 159 Bytes
  • 54/8.1 TensorFlow MNIST Part 6 with Comments.html 159 Bytes
  • 54/10.5 TensorFlow MNIST 'Around 98% Accuracy' Solution.html 157 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/9.1 Basic NN Example with TensorFlow (Complete).html 156 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/32.2 Preprocessing.html 156 Bytes
  • 40. Part 6 Mathematics/14.1 Dot Product Python Notebook.html 154 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Basic NN Example Exercise 3a Solution.html 154 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Basic NN Example Exercise 3c Solution.html 154 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Basic NN Example Exercise 3b Solution.html 154 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.7 Basic NN Example Exercise 3d Solution.html 154 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/10.5 Basic NN Example with TensorFlow (All Exercises).html 154 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/6.1 Basic NN Example with TensorFlow (Part 1).html 154 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/7.1 Basic NN Example with TensorFlow (Part 2).html 154 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/8.1 Basic NN Example with TensorFlow (Part 3).html 154 Bytes
  • 54/9.1 TensorFlow MNIST Complete Code with Comments.html 152 Bytes
  • 54/10.10 TensorFlow MNIST '2. Depth' Solution.html 150 Bytes
  • 54/10.8 TensorFlow MNIST '1. Width' Solution.html 150 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Basic NN Example Exercise 4 Solution.html 149 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.2 Basic NN Example Exercise 5 Solution.html 149 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Basic NN Example Exercise 1 Solution.html 149 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.8 Basic NN Example Exercise 2 Solution.html 149 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Basic NN Example Exercise 6 Solution.html 149 Bytes
  • 40. Part 6 Mathematics/8.1 Tensors Notebook.html 148 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4.1 Basic NN Example (Part 4).html 145 Bytes
  • 54/11.1 TensorFlow MNIST All Exercises.html 144 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 Basic NN Example (All Exercises).html 143 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html 142 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.2 Bais NN Example Part 1.html 136 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Basic NN Example (Part 2).html 136 Bytes
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Basic NN Example (Part 3).html 136 Bytes
  • 1/3.1 Download All Resources.html 134 Bytes
  • 23. Python - Variables and Data Types/1.2 Variables - Resources.html 134 Bytes
  • 23. Python - Variables and Data Types/3.1 Numbers and Boolean Values - Resources.html 134 Bytes
  • 23. Python - Variables and Data Types/5.1 Strings - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/1.1 Arithmetic Operators - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/10.1 Indexing Elements - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/12.1 Structure Your Code with Indentation - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/3.1 The Double Equality Sign - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/5.1 Reassign Values - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/7.1 Add Comments - Resources.html 134 Bytes
  • 24. Python - Basic Python Syntax/9.1 Line Continuation - Resources.html 134 Bytes
  • 25. Python - Other Python Operators/1.1 Comparison Operators - Resources.html 134 Bytes
  • 25. Python - Other Python Operators/3.1 Logical and Identity Operators - Resources.html 134 Bytes
  • 26. Python - Conditional Statements/1.1 Introduction to the If Statement - Resources.html 134 Bytes
  • 26. Python - Conditional Statements/3.1 Add an Else Statement - Resources.html 134 Bytes
  • 26. Python - Conditional Statements/4.1 Else if, for Brief - Elif - Resources.html 134 Bytes
  • 26. Python - Conditional Statements/5.1 A Note on Boolean Values - Resources.html 134 Bytes
  • 27. Python - Python Functions/1.1 Defining a Function in Python - Resources.html 134 Bytes
  • 27. Python - Python Functions/2.1 Creating a Function with a Parameter - Resources.html 134 Bytes
  • 27. Python - Python Functions/3.1 Another Way to Define a Function - Resources.html 134 Bytes
  • 27. Python - Python Functions/4.1 Using a Function in Another Function - Resources.html 134 Bytes
  • 27. Python - Python Functions/5.1 Combining Conditional Statements and Functions - Resources.html 134 Bytes
  • 27. Python - Python Functions/6.1 Creating Functions Containing a Few Arguments - Resources.html 134 Bytes
  • 27. Python - Python Functions/7.1 Notable Built-In Functions in Python - Resources.html 134 Bytes
  • 28. Python - Sequences/1.1 Lists - Resources.html 134 Bytes
  • 28. Python - Sequences/3.1 Help Yourself with Methods - Resources.html 134 Bytes
  • 28. Python - Sequences/5.1 List Slicing - Resources.html 134 Bytes
  • 28. Python - Sequences/6.1 Tuples - Resources.html 134 Bytes
  • 28. Python - Sequences/7.1 Dictionaries - Resources.html 134 Bytes
  • 29. Python - Iterations/1.1 For Loops - Resources.html 134 Bytes
  • 29. Python - Iterations/3.1 While Loops and Incrementing - Resources.html 134 Bytes
  • 29. Python - Iterations/4.1 Create Lists with the range() Function - Resources.html 134 Bytes
  • 29. Python - Iterations/6.1 Use Conditional Statements and Loops Together - Resources.html 134 Bytes
  • 29. Python - Iterations/7.1 All In - Conditional Statements, Functions, and Loops - Resources.html 134 Bytes
  • 29. Python - Iterations/8.1 Iterating over Dictionaries - Resources.html 134 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/8.1 First regression in Python.html 134 Bytes
  • 32. Advanced Statistical Methods - Linear regression with StatsModels/9.1 First regression in Python - Exercise.html 134 Bytes
  • 32/8.1 First regression in Python.html 134 Bytes
  • 32/9.1 First regression in Python - Exercise.html 134 Bytes
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/5.1 Dummies and VIF - Exercise and Solution.html 134 Bytes
  • 35/1.1 sklearn - Linear Regression - Practical Example (Part 1).html 134 Bytes
  • 35/2.1 sklearn - Linear Regression - Practical Example (Part 2).html 134 Bytes
  • 35/4.1 sklearn - Linear Regression - Practical Example (Part 3).html 134 Bytes
  • 35/5.1 Dummies and VIF - Exercise and Solution.html 134 Bytes
  • 35/6.1 sklearn - Linear Regression - Practical Example (Part 4).html 134 Bytes
  • 35/8.1 sklearn - Linear Regression - Practical Example (Part 5).html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/10.1 Binary predictors.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/11.2 Binary predictors - exercise.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/12.1 Accuracy.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/13.1 Accuracy of the model - exercise.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/15.1 Testing the model.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/16.3 Testing the model - exercise.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/2.1 A simple example in Python.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/4.1 Building a logistic regression.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/5.1 Building a logistic regression.html 134 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/8.1 Understanding logistic regression.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/11.1 Market segmentation.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/12.1 Market segmentation.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/14.2 Exercise - part 1.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/15.2 Exercise - part 2.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/2.1 Example of clustering.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/3.1 A simple example of clustering.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/4.1 Clustering categorical data.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/5.2 Clustering categorical data.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/6.1 How to choose the number of clusters.html 134 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/7.1 How to choose the number of clusters.html 134 Bytes
  • 39. Advanced Statistical Methods - Other Types of Clustering/3.1 Heatmaps.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/4.1 A note on TensorFlow 2 Syntax.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/5.1 Types of File Formats.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/6.1 Outlining the Model.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/7.1 Interpreting the Result.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/8.1 Customizing a TensorFlow 2 Model.html 134 Bytes
  • 44. Deep Learning - TensorFlow 2.0 Introduction/9.1 Basic NN with TensorFlow.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/10.1 MNIST Learning.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/11.1 MNIST - Exercises.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/12.1 MNIST Testing the Model.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/3.1 MNIST Importing the Relevant Packages.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/5.1 MNIST Preprocess the Data.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/7.1 MNIST Preprocess the Data.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/8.1 MNIST Outline the Model.html 134 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/9.1 MNIST Select the Loss and the Optimizer.html 134 Bytes
  • 51. Deep Learning - Business Case Example/1.2 Business Case Exploring the Dataset.html 134 Bytes
  • 51. Deep Learning - Business Case Example/11.1 Business Case Testing the Model.html 134 Bytes
  • 51. Deep Learning - Business Case Example/12.1 Business Case Final Exercise.html 134 Bytes
  • 51. Deep Learning - Business Case Example/4.1 Business Case Preprocessing the Data.html 134 Bytes
  • 51. Deep Learning - Business Case Example/5.1 Business Case Preprocessing the Data.html 134 Bytes
  • 51. Deep Learning - Business Case Example/7.1 Business Case Load the Preprocessed Data.html 134 Bytes
  • 51. Deep Learning - Business Case Example/8.1 Business Case Learning and Interpreting.html 134 Bytes
  • 51. Deep Learning - Business Case Example/9.1 Business Case Setting an Early Stopping Mechanism.html 134 Bytes
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/5.1 Actual Introduction to TensorFlow.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/11.1 TensorFlow Business Case Homework.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/12.1 TensorFlow Business Case Homework.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/4.1 Audiobooks Preprocessing.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/5.1 Preprocessing Exercise.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/6.1 Creating a Data Provider (Class).html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/7.1 TensorFlow Business Case Model Outline.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/8.1 TensorFlow Business Case Optimization.html 134 Bytes
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/9.1 TensorFlow Business Case Interpretation.html 134 Bytes
  • 60. Case Study - Loading the 'absenteeism_module'/1.1 5 Files Needed to Deploy the Model.html 134 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 117 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html 116 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/9. SOLUTION - Dropping a Column from a DataFrame in Python.html 113 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html 87 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html 87 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/5. Building a Logistic Regression - Exercise.html 87 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/8. Understanding Logistic Regression Tables - Exercise.html 87 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html 87 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data - Exercise.html 87 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html 87 Bytes
  • 15. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/13. Histogram Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/21. Skewness Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/29. Covariance Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html 81 Bytes
  • 15. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html 81 Bytes
  • 16/2. Practical Example Descriptive Statistics Exercise.html 81 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html 81 Bytes
  • 18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes
  • 19. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html 81 Bytes
  • 20. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes
  • 20. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html 81 Bytes
  • 20. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 1). Exercise.html 81 Bytes
  • 20. Statistics - Hypothesis Testing/20. Test for the mean. Independent samples (Part 2) Exercise.html 81 Bytes
  • 20. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html 81 Bytes
  • 21/2. Practical Example Hypothesis Testing Exercise.html 81 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html 79 Bytes
  • 50. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html 79 Bytes
  • 51. Deep Learning - Business Case Example/7. Business Case Load the Preprocessed Data - Exercise.html 79 Bytes
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/5. Dummies and Variance Inflation Factor - Exercise.html 76 Bytes
  • 35/5. Dummies and Variance Inflation Factor - Exercise.html 76 Bytes

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