[Tutorialsplanet.NET] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

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[Tutorialsplanet.NET] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

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文件列表

  • 16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 168.3 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 151.4 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 108.6 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 107.7 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 104.2 MB
  • 13. Probability - Probability in Other Fields/1. Probability in Finance.mp4 103.9 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
  • 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 85.1 MB
  • 18/3. 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
  • 12. Probability - Distributions/3. Types of Probability Distributions.mp4 74.5 MB
  • 18/12. Confidence intervals. Two means. Dependent samples.mp4 73.9 MB
  • 21. Statistics - Practical Example Hypothesis Testing/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 67.7 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. Part 1 Introduction/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
  • 45/3. Digging into a Deep Net.mp4 62.3 MB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB
  • 9. Part 2 Probability/7. Events and Their Complements.mp4 62.0 MB
  • 52. Deep Learning - Conclusion/4. An overview of CNNs.mp4 61.7 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
  • 35/8. Practical Example Linear Regression (Part 5).mp4 60.7 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
  • 18/5. 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
  • 35/6. 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
  • 33/18. Dealing with Categorical Data - Dummy Variables.mp4 58.4 MB
  • 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 58.3 MB
  • 62. Appendix - Additional Python Tools/5. List Comprehensions.mp4 58.2 MB
  • 33/3. Adjusted R-Squared.mp4 57.5 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
  • 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
  • 18/1. 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
  • 32/13. Decomposition of Variability.mp4 52.1 MB
  • 40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 51.8 MB
  • 34/19. Train - Test Split Explained.mp4 51.6 MB
  • 59/12. Testing the Model We Created.mp4 51.5 MB
  • 1. Part 1 Introduction/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 50.2 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
  • 62. Appendix - Additional Python Tools/1. Using the .format() Method.mp4 50.0 MB
  • 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp4 49.7 MB
  • 18/10. Margin of Error.mp4 49.5 MB
  • 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp4 49.3 MB
  • 54/8. MNIST Learning.mp4 49.0 MB
  • 62. Appendix - Additional Python Tools/4. Triple Nested For Loops.mp4 48.9 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 47.0 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
  • 32/8. 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
  • 33/13. A3 Normality and Homoscedasticity.mp4 44.8 MB
  • 28. Python - Sequences/7. Dictionaries.mp4 43.7 MB
  • 59/6. Fitting the Model and Assessing its Accuracy.mp4 43.7 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
  • 32/17. 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 42.9 MB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.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
  • 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp4 42.2 MB
  • 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.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
  • 34/14. Feature Scaling (Standardization).mp4 41.0 MB
  • 59/7. Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp4 40.6 MB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 40.6 MB
  • 62. Appendix - Additional Python Tools/6. Anonymous (Lambda) Functions.mp4 40.4 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
  • 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
  • 28. Python - Sequences/1. Lists.mp4 39.6 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 39.5 MB
  • 28. Python - Sequences/3. Using Methods.mp4 39.4 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 39.1 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
  • 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38.4 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
  • 33/11. A2 No Endogeneity.mp4 37.4 MB
  • 18/6. Student's T Distribution.mp4 37.2 MB
  • 45/7. Backpropagation.mp4 36.7 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
  • 34/15. Feature Selection through Standardization of Weights.mp4 36.6 MB
  • 11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp4 36.5 MB
  • 34/3. Simple Linear Regression with sklearn.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
  • 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 33.9 MB
  • 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 33.8 MB
  • 18/8. Confidence Intervals; Population Variance Unknown; T-score.mp4 33.8 MB
  • 34/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 33.6 MB
  • 33/14. A4 No Autocorrelation.mp4 33.1 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
  • 34/8. Calculating the Adjusted R-Squared in sklearn.mp4 32.4 MB
  • 28. Python - Sequences/5. List Slicing.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
  • 29. Python - Iterations/8. How to Iterate over Dictionaries.mp4 31.1 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 31.1 MB
  • 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 31.0 MB
  • 45/2. What is a Deep Net.mp4 31.0 MB
  • 34/10. Feature Selection (F-regression).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
  • 28. Python - Sequences/6. Tuples.mp4 30.9 MB
  • 62. Appendix - Additional Python Tools/3. Introduction to Nested For Loops.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 30.4 MB
  • 18/14. Confidence intervals. Two means. Independent Samples (Part 1).mp4 30.2 MB
  • 33/16. A5 No Multicollinearity.mp4 30.1 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
  • 29. Python - Iterations/3. While Loops and Incrementing.mp4 29.8 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
  • 29. Python - Iterations/6. Conditional Statements and Loops.mp4 29.1 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
  • 34/1. What is sklearn and How is it Different from Other Packages.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/16. The Additive Rule.mp4 28.3 MB
  • 11. Probability - Bayesian Inference/5. Intersection of Sets.mp4 28.3 MB
  • 18/16. Confidence intervals. Two means. Independent Samples (Part 2).mp4 28.1 MB
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  • 58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.srt 8.2 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.srt 8.1 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.srt 8.1 kB
  • 29. Python - Iterations/8. How to Iterate over Dictionaries.srt 8.1 kB
  • 32/8. First Regression in Python.srt 8.1 kB
  • 54/6.1 12.6. TensorFlow_MNIST_with_comments_Part_4.ipynb 8.1 kB
  • 59/8. Interpreting the Coefficients for Our Problem.srt 8.1 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.srt 8.0 kB
  • 51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.srt 8.0 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.srt 8.0 kB
  • 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.srt 8.0 kB
  • 34/7.3 sklearn - Multiple Linear Regression.ipynb 8.0 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.srt 7.9 kB
  • 34/14. Feature Scaling (Standardization).srt 7.9 kB
  • 29. Python - Iterations/4. Lists with the range() Function.srt 7.8 kB
  • 36. Advanced Statistical Methods - Logistic Regression/15.3 Testing the model_with_comments.ipynb 7.7 kB
  • 23. Python - Variables and Data Types/5.3 Strings - Lecture_Py3.ipynb 7.7 kB
  • 12. Probability - Distributions/1. Fundamentals of Probability Distributions.srt 7.7 kB
  • 15. Statistics - Descriptive Statistics/22. Variance.srt 7.7 kB
  • 60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.srt 7.7 kB
  • 33/3. Adjusted R-Squared.srt 7.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).srt 7.7 kB
  • 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.srt 7.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/6.2 Selecting the number of clusters_with_comments.ipynb 7.7 kB
  • 29. Python - Iterations/6. Conditional Statements and Loops.srt 7.6 kB
  • 59/6. Fitting the Model and Assessing its Accuracy.srt 7.6 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.srt 7.5 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt 7.5 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.srt 7.5 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/14.2 Species Segmentation with Cluster Analysis Part 1- Solution.ipynb 7.5 kB
  • 34/3. Simple Linear Regression with sklearn.srt 7.5 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/29.2 Absenteeism Exercise - Preprocessing - ChP - df_date_reason_mod.ipynb 7.5 kB
  • 54/5.1 12.5. TensorFlow_MNIST_with_comments_Part_3.ipynb 7.5 kB
  • 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.srt 7.5 kB
  • 34/15. Feature Selection through Standardization of Weights.srt 7.4 kB
  • 59/10. Interpreting the Coefficients of the Logistic Regression.srt 7.4 kB
  • 34/19.2 sklearn - Train Test Split.ipynb 7.4 kB
  • 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.srt 7.4 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.srt 7.4 kB
  • 11. Probability - Bayesian Inference/20. Bayes' Law.srt 7.4 kB
  • 23. Python - Variables and Data Types/5. Python Strings.srt 7.3 kB
  • 33/18.3 Dummy variables_with_comments.ipynb 7.3 kB
  • 32/1. The Linear Regression Model.srt 7.2 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.srt 7.2 kB
  • 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.srt 7.1 kB
  • 22. Part 4 Introduction to Python/3. Why Python.srt 7.1 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.srt 7.1 kB
  • 28. Python - Sequences/6. Tuples.srt 7.1 kB
  • 22. Part 4 Introduction to Python/1. Introduction to Programming.srt 7.1 kB
  • 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.srt 7.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/12.2 Market segmentation example_Part2_with_comments.ipynb 7.0 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Minimal_example_Part_3.ipynb 7.0 kB
  • 36. Advanced Statistical Methods - Logistic Regression/16.2 Testing the Model - Exercise..ipynb 7.0 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).srt 7.0 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/12.1 TensorFlow_MNIST_complete.ipynb 6.9 kB
  • 9. Part 2 Probability/7. Events and Their Complements.srt 6.9 kB
  • 56. Software Integration/9. Software Integration - Explained.srt 6.9 kB
  • 45/3. Digging into a Deep Net.srt 6.9 kB
  • 34/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt 6.9 kB
  • 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.srt 6.8 kB
  • 9. Part 2 Probability/3. Computing Expected Values.srt 6.8 kB
  • 33/13. A3 Normality and Homoscedasticity.srt 6.8 kB
  • 34/10. Feature Selection (F-regression).srt 6.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.srt 6.8 kB
  • 13. Probability - Probability in Other Fields/3. Probability in Data Science.srt 6.8 kB
  • 2/1. Data Science and Business Buzzwords Why are there so Many.srt 6.8 kB
  • 59/7. Creating a Summary Table with the Coefficients and Intercept.srt 6.8 kB
  • 60. Case Study - Loading the 'absenteeism_module'/1.5 absenteeism_module.py 6.8 kB
  • 26. Python - Conditional Statements/4. The ELIF Statement.srt 6.8 kB
  • 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.srt 6.8 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.srt 6.8 kB
  • 29. Python - Iterations/1. For Loops.srt 6.7 kB
  • 32/17. R-Squared.srt 6.7 kB
  • 12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.srt 6.7 kB
  • 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.srt 6.7 kB
  • 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason Behind These Disciplines.srt 6.7 kB
  • 59/12. Testing the Model We Created.srt 6.7 kB
  • 52. Deep Learning - Conclusion/4. An overview of CNNs.srt 6.6 kB
  • 9. Part 2 Probability/5. Frequency.srt 6.6 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.srt 6.5 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/5.1 TensorFlow_MNIST_Part2_with_comments.ipynb 6.5 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.srt 6.5 kB
  • 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt 6.5 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.srt 6.5 kB
  • 32/11. How to Interpret the Regression Table.srt 6.5 kB
  • 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.srt 6.5 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt 6.4 kB
  • 34/8. Calculating the Adjusted R-Squared in sklearn.srt 6.4 kB
  • 51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.srt 6.4 kB
  • 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.srt 6.4 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.srt 6.4 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.srt 6.4 kB
  • 36. Advanced Statistical Methods - Logistic Regression/5.3 Example_bank_data.csv 6.4 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/7.1 5.4. TensorFlow_Minimal_example_Part_2.ipynb 6.3 kB
  • 28. Python - Sequences/7.3 Dictionaries - Solution_Py3.ipynb 6.3 kB
  • 18/10. Margin of Error.srt 6.3 kB
  • 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.srt 6.3 kB
  • 54/4.1 12.4. TensorFlow_MNIST_with_comments_Part_2.ipynb 6.2 kB
  • 30. Python - Advanced Python Tools/1. Object Oriented Programming.srt 6.2 kB
  • 18/14. Confidence intervals. Two means. Independent Samples (Part 1).srt 6.2 kB
  • 34/17.2 sklearn - Feature Scaling Exercise.ipynb 6.2 kB
  • 34/3.2 sklearn - Simple Linear Regression_with_comments.ipynb 6.2 kB
  • 62. Appendix - Additional Python Tools/2. Iterating Over Range Objects.srt 6.2 kB
  • 50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.srt 6.2 kB
  • 49. Deep Learning - Preprocessing/3. Standardization.srt 6.1 kB
  • 15. Statistics - Descriptive Statistics/1. Types of Data.srt 6.1 kB
  • 48/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt 6.1 kB
  • 56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.srt 6.1 kB
  • 29. Python - Iterations/3. While Loops and Incrementing.srt 6.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/11.3 Market segmentation example_with_comments.ipynb 6.0 kB
  • 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.srt 6.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.srt 6.0 kB
  • 25. Python - Other Python Operators/3.1 Logical and Identity Operators - Lecture_Py3.ipynb 6.0 kB
  • 25. Python - Other Python Operators/3.2 Logical and Identity Operators - Lecture_Py3.ipynb 6.0 kB
  • 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.srt 6.0 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.srt 6.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/2.2 Country clusters_with_comments.ipynb 5.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.srt 5.9 kB
  • 33/20.1 Making predictions.ipynb 5.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/15.2 Testing the model.ipynb 5.9 kB
  • 25. Python - Other Python Operators/3. Logical and Identity Operators.srt 5.9 kB
  • 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.srt 5.9 kB
  • 15. Statistics - Descriptive Statistics/17. Mean, median and mode.srt 5.9 kB
  • 18/8. Confidence Intervals; Population Variance Unknown; T-score.srt 5.8 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.srt 5.8 kB
  • 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.srt 5.8 kB
  • 34/13.2 sklearn - Multiple Linear Regression Exercise.ipynb 5.8 kB
  • 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.srt 5.8 kB
  • 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.srt 5.8 kB
  • 32/7. Python Packages Installation.srt 5.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/4.1 Categorical data_with_comments.ipynb 5.8 kB
  • 59/16. Preparing the Deployment of the Model through a Module.srt 5.8 kB
  • 10. Probability - Combinatorics/11. Solving Combinations.srt 5.7 kB
  • 34/16. Predicting with the Standardized Coefficients.srt 5.7 kB
  • 46. Deep Learning - Overfitting/1. What is Overfitting.srt 5.7 kB
  • 51. Deep Learning - Business Case Example/4.2 TensorFlow_Audiobooks_Preprocessing.ipynb 5.7 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/4.1 TensorFlow_Audiobooks_Preprocessing.ipynb 5.7 kB
  • 59/13. Saving the Model and Preparing it for Deployment.srt 5.7 kB
  • 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.srt 5.7 kB
  • 28. Python - Sequences/5. List Slicing.srt 5.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/7.1 How to Choose the Number of Clusters - Exercise.ipynb 5.7 kB
  • 11. Probability - Bayesian Inference/7. Union of Sets.srt 5.7 kB
  • 27. Python - Python Functions/7.3 Notable Built-In Functions in Python - Solution_Py3.ipynb 5.7 kB
  • 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent Samples (Part 1).srt 5.6 kB
  • 56. Software Integration/7. Communication between Software Products through Text Files.srt 5.6 kB
  • 14. Part 3 Statistics/1. Population and Sample.srt 5.6 kB
  • 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.srt 5.6 kB
  • 57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt 5.6 kB
  • 23. Python - Variables and Data Types/5.1 Strings - Solution_Py3.ipynb 5.6 kB
  • 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.srt 5.5 kB
  • 18/5. Confidence Interval Clarifications.srt 5.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/13.3 Calculating the Accuracy of the Model - Exercise.ipynb 5.5 kB
  • 40. Part 6 Mathematics/13. Transpose of a Matrix.srt 5.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/2.3 Admittance_with_comments.ipynb 5.4 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.srt 5.4 kB
  • 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.srt 5.4 kB
  • 12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.srt 5.4 kB
  • 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.srt 5.4 kB
  • 45/5. Activation Functions.srt 5.4 kB
  • 33/11. A2 No Endogeneity.srt 5.4 kB
  • 59/11. Backward Elimination or How to Simplify Your Model.srt 5.4 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.srt 5.4 kB
  • 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.srt 5.4 kB
  • 52. Deep Learning - Conclusion/1. Summary on What You've Learned.srt 5.3 kB
  • 48/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt 5.3 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.srt 5.3 kB
  • 54/6. Calculating the Accuracy of the Model.srt 5.3 kB
  • 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent Samples (Part 2).srt 5.3 kB
  • 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.srt 5.2 kB
  • 2/9. A Breakdown of our Data Science Infographic.srt 5.2 kB
  • 1. Part 1 Introduction/2. What Does the Course Cover.srt 5.2 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.srt 5.2 kB
  • 2/3. What is the difference between Analysis and Analytics.srt 5.2 kB
  • 11. Probability - Bayesian Inference/1. Sets and Events.srt 5.2 kB
  • 20. Statistics - Hypothesis Testing/10. p-value.srt 5.2 kB
  • 59/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt 5.1 kB
  • 28. Python - Sequences/5.3 List Slicing - Lecture_Py3.ipynb 5.1 kB
  • 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.srt 5.1 kB
  • 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.srt 5.1 kB
  • 11. Probability - Bayesian Inference/13. The Conditional Probability Formula.srt 5.1 kB
  • 15. Statistics - Descriptive Statistics/27. Covariance.srt 5.0 kB
  • 34/3.1 sklearn - Simple Linear Regression.ipynb 5.0 kB
  • 33/14. A4 No Autocorrelation.srt 5.0 kB
  • 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.srt 5.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/5.3 Clustering Categorical Data - Solution.ipynb 5.0 kB
  • 46. Deep Learning - Overfitting/3. What is Validation.srt 5.0 kB
  • 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.srt 5.0 kB
  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.srt 4.9 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/23.1 Absenteeism Exercise - Preprocessing - df_reason_mod.ipynb 4.9 kB
  • 30. Python - Advanced Python Tools/7. Importing Modules in Python.srt 4.9 kB
  • 48/1. Stochastic Gradient Descent.srt 4.9 kB
  • 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.srt 4.9 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.srt 4.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/8.2 Understanding Logistic Regression Tables - Solution.ipynb 4.9 kB
  • 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.srt 4.9 kB
  • 12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.srt 4.9 kB
  • 60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.srt 4.9 kB
  • 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.srt 4.8 kB
  • 51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.srt 4.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/12.1 Market segmentation example_Part2.ipynb 4.8 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.srt 4.8 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/3.1 A Simple Example of Clustering - Solution.ipynb 4.8 kB
  • 22. Part 4 Introduction to Python/5. Why Jupyter.srt 4.7 kB
  • 41. Part 7 Deep Learning/1. What to Expect from this Part.srt 4.7 kB
  • 11. Probability - Bayesian Inference/18. The Multiplication Law.srt 4.7 kB
  • 33/16. A5 No Multicollinearity.srt 4.7 kB
  • 33/18.2 Dummy Variables.ipynb 4.7 kB
  • 51. Deep Learning - Business Case Example/7.1 TensorFlow_Audiobooks_Machine_Learning_Part1_with_comments.ipynb 4.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.srt 4.7 kB
  • 28. Python - Sequences/6.2 Tuples - Solution_Py3.ipynb 4.7 kB
  • 59/1. Exploring the Problem with a Machine Learning Mindset.srt 4.7 kB
  • 40. Part 6 Mathematics/7.1 Scalars, Vectors, and Matrices.ipynb 4.7 kB
  • 15. Statistics - Descriptive Statistics/3. Levels of Measurement.srt 4.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/6.1 Selecting the number of clusters.ipynb 4.6 kB
  • 23. Python - Variables and Data Types/1. Variables.srt 4.6 kB
  • 10. Probability - Combinatorics/9. Solving Variations without Repetition.srt 4.6 kB
  • 27. Python - Python Functions/7.2 Notable Built-In Functions in Python - Lecture_Py3.ipynb 4.6 kB
  • 36. Advanced Statistical Methods - Logistic Regression/11.1 Binary Predictors in a Logistic Regression - Solution.ipynb 4.6 kB
  • 18/16. Confidence intervals. Two means. Independent Samples (Part 2).srt 4.6 kB
  • 51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.srt 4.6 kB
  • 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.srt 4.6 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.srt 4.6 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.srt 4.6 kB
  • 45/7. Backpropagation.srt 4.6 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).srt 4.6 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/14.3 Species Segmentation with Cluster Analysis Part 1- Exercise.ipynb 4.6 kB
  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).srt 4.6 kB
  • 45/6. Activation Functions Softmax Activation.srt 4.6 kB
  • 33/20. Making Predictions with the Linear Regression.srt 4.6 kB
  • 36. Advanced Statistical Methods - Logistic Regression/5.1 Building a Logistic Regression - Solution.ipynb 4.5 kB
  • 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.srt 4.5 kB
  • 28. Python - Sequences/3.3 Help Yourself with Methods - Lecture_Py3.ipynb 4.5 kB
  • 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.srt 4.5 kB
  • 28. Python - Sequences/7.2 Dictionaries - Lecture_Py3.ipynb 4.5 kB
  • 40. Part 6 Mathematics/1. What is a Matrix.srt 4.4 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.srt 4.4 kB
  • 10. Probability - Combinatorics/13. Symmetry of Combinations.srt 4.4 kB
  • 27. Python - Python Functions/2. How to Create a Function with a Parameter.srt 4.4 kB
  • 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.srt 4.4 kB
  • 40. Part 6 Mathematics/14. Dot Product.srt 4.4 kB
  • 28. Python - Sequences/5.1 List Slicing - Solution_Py3.ipynb 4.4 kB
  • 24. Python - Basic Python Syntax/1.2 Arithmetic Operators - Solution_Py3.ipynb 4.3 kB
  • 27. Python - Python Functions/7. Built-in Functions in Python.srt 4.3 kB
  • 59/4. Standardizing the Data.srt 4.3 kB
  • 46. Deep Learning - Overfitting/5. N-Fold Cross Validation.srt 4.3 kB
  • 34/7. Multiple Linear Regression with sklearn.srt 4.3 kB
  • 32/13. Decomposition of Variability.srt 4.3 kB
  • 10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.srt 4.2 kB
  • 57. Case Study - What's Next in the Course/3. Introducing the Data Set.srt 4.2 kB
  • 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.srt 4.2 kB
  • 18/6. Student's T Distribution.srt 4.2 kB
  • 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.srt 4.2 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/32.2 Absenteeism Exercise - EXERCISES and SOLUTIONS.ipynb 4.2 kB
  • 35/4. Practical Example Linear Regression (Part 3).srt 4.2 kB
  • 24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.srt 4.2 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.srt 4.2 kB
  • 36. Advanced Statistical Methods - Logistic Regression/4.3 Admittance regression tables_fixed_error.ipynb 4.2 kB
  • 40. Part 6 Mathematics/5. Linear Algebra and Geometry.srt 4.2 kB
  • 10. Probability - Combinatorics/3. Permutations and How to Use Them.srt 4.2 kB
  • 32/8.2 Simple linear regression_with_comments.ipynb 4.2 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.srt 4.2 kB
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  • 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.srt 4.1 kB
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  • 50. Deep Learning - Classifying on the MNIST Dataset/3.1 TensorFlow_MNIST_Part1_with_comments.ipynb 4.1 kB
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  • 40. Part 6 Mathematics/3. Scalars and Vectors.srt 3.9 kB
  • 57. Case Study - What's Next in the Course/2. The Business Task.srt 3.8 kB
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  • 22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.srt 3.8 kB
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  • 47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt 3.8 kB
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  • 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Minimal_example_Part_2.ipynb 3.7 kB
  • 15. Statistics - Descriptive Statistics/19. Skewness.srt 3.7 kB
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.srt 3.7 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.srt 3.7 kB
  • 36. Advanced Statistical Methods - Logistic Regression/12.1 Accuracy.ipynb 3.7 kB
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  • 50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.srt 3.7 kB
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  • 50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.srt 3.6 kB
  • 27. Python - Python Functions/5. Conditional Statements and Functions.srt 3.6 kB
  • 47. Deep Learning - Initialization/1. What is Initialization.srt 3.6 kB
  • 44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.srt 3.6 kB
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  • 53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.srt 3.5 kB
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  • 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.srt 3.3 kB
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  • 40. Part 6 Mathematics/12.1 Errors when adding scalars, vectors, and matrices in Python.ipynb 3.2 kB
  • 36. Advanced Statistical Methods - Logistic Regression/8.1 Understanding Logistic Regression Tables - Exercise.ipynb 3.2 kB
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  • 29. Python - Iterations/6.2 Use Conditional Statements and Loops Together - Solution_Py3.ipynb 3.0 kB
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  • 40. Part 6 Mathematics/13.1 Tranpose of a matrix.ipynb 3.0 kB
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  • 58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.srt 2.9 kB
  • 12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.srt 2.9 kB
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  • 42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.srt 2.8 kB
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  • 11. Probability - Bayesian Inference/16. The Additive Rule.srt 2.8 kB
  • 12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.srt 2.8 kB
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  • 42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.srt 2.8 kB
  • 46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.srt 2.7 kB
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  • 52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.srt 2.6 kB
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  • 11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.srt 2.6 kB
  • 36. Advanced Statistical Methods - Logistic Regression/4.1 Admittance regression_summary_error.ipynb 2.5 kB
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  • 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
  • 23. Python - Variables and Data Types/3.3 Numbers and Boolean Values - Exercise_Py3.ipynb 2.3 kB
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  • 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.srt 2.3 kB
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  • 27. Python - Python Functions/5.2 Combining Conditional Statements and Functions - Exercise_Py3.ipynb 1.1 kB
  • 52. Deep Learning - Conclusion/3. DeepMind and Deep Learning.html 1.1 kB
  • 27. Python - Python Functions/4.3 0.6.4 Using a Function in another Function - Exercise_Py3.ipynb 1.1 kB
  • 24. Python - Basic Python Syntax/7.1 Add Comments - Lecture_Py3.ipynb 1.1 kB
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  • 60. Case Study - Loading the 'absenteeism_module'/1.2 model.original 1.0 kB
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  • 60. Case Study - Loading the 'absenteeism_module'/4. Exporting the Obtained Data Set as a .csv.html 998 Bytes
  • 60. Case Study - Loading the 'absenteeism_module'/4.1 Absenteeism Exercise - Deploying the 'absenteeism_module'.ipynb 973 Bytes
  • 24. Python - Basic Python Syntax/12.2 Structure Your Code with Indentation - Exercise_Py3.ipynb 956 Bytes
  • 32/8.3 1.01. Simple linear regression.csv 922 Bytes
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  • 58. Case Study - Preprocessing the 'Absenteeism_data'/33. A Note on Exporting Your Data as a .csv File.html 883 Bytes
  • 27. Python - Python Functions/1.1 Defining a Function in Python - Lecture_Py3.ipynb 868 Bytes
  • 58. Case Study - Preprocessing the 'Absenteeism_data'/8. EXERCISE - Dropping a Column from a DataFrame in Python.html 866 Bytes
  • 35/3. A Note on Multicollinearity.html 849 Bytes
  • 24. Python - Basic Python Syntax/3.1 The Double Equality Sign - Exercise_Py3.ipynb 838 Bytes
  • 26. Python - Conditional Statements/5.1 A Note on Boolean Values - Lecture_Py3.ipynb 791 Bytes
  • 24. Python - Basic Python Syntax/9.1 Line Continuation - Lecture_Py3.ipynb 779 Bytes
  • 34/5. A Note on Normalization.html 733 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
  • 35/9. 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
  • 34/11. A Note on Calculation of P-values with sklearn.html 372 Bytes
  • 51. Deep Learning - Business Case Example/5. Business Case Preprocessing the Data - Exercise.html 370 Bytes
  • 36. Advanced Statistical Methods - Logistic Regression/15.1 2.03. Test dataset.csv 322 Bytes
  • 59/15. EXERCISE - Saving the Model (and Scaler).html 284 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/11.1 3.12. Example.csv 283 Bytes
  • 39. Advanced Statistical Methods - Other Types of Clustering/3.3 Country clusters standardized.csv 244 Bytes
  • 59/11.1 Logistic Regression prior to Backward Elimination.html 226 Bytes
  • 59/9.1 Logistic Regression prior to Custom Scaler.html 219 Bytes
  • 59/15.1 Logistic Regression with Comments.html 210 Bytes
  • 38. Advanced Statistical Methods - K-Means Clustering/2.3 3.01. Country clusters.csv 200 Bytes
  • 59/15.2 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'/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
  • 18/11. Margin of Error.html 165 Bytes
  • 18/2. What are Confidence Intervals.html 165 Bytes
  • 18/7. 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
  • 32/12. 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
  • 33/10. A1 Linearity.html 165 Bytes
  • 33/12. A2 No Endogeneity.html 165 Bytes
  • 33/15. A4 No autocorrelation.html 165 Bytes
  • 33/17. A5 No Multicollinearity.html 165 Bytes
  • 33/2. Multiple Linear Regression.html 165 Bytes
  • 33/4. Adjusted R-Squared.html 165 Bytes
  • 33/8. OLS Assumptions.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
  • 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
  • 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
  • 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
  • 1. Part 1 Introduction/3.1 Download all resources.html 134 Bytes
  • 35/4.3 sklearn - Linear Regression - Practical Example (Part 3).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. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html 81 Bytes
  • 17. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html 81 Bytes
  • 18/13. Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes
  • 18/15. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 Bytes
  • 18/17. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 Bytes
  • 18/4. Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 Bytes
  • 18/9. Confidence Intervals; Population Variance Unknown; T-score; 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. Statistics - Practical Example Hypothesis Testing/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
  • 33/19. Dealing with Categorical Data - Dummy Variables.html 76 Bytes
  • 33/5. Multiple Linear Regression Exercise.html 76 Bytes
  • 34/13. Multiple Linear Regression - Exercise.html 76 Bytes
  • 34/17. Feature Scaling (Standardization) - Exercise.html 76 Bytes
  • 34/6. Simple Linear Regression with sklearn - Exercise.html 76 Bytes
  • 34/9. Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 Bytes
  • 35/5. Dummies and Variance Inflation Factor - Exercise.html 76 Bytes

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