[FTU] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

Creation Time	Dec. 11, 2023, 9:42 p.m.
Last Access Time	March 10, 2025, 11:55 a.m.
File Size	13.7 GB
Keywords	FTU Complete Science - Udemy Course 2019 The Bootcamp Data
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Total Files	1568
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16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 160.5 MB
40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4 144.3 MB
10. Combinatorics/20. A Practical Example of Combinatorics.mp4 134.1 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125.1 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 123.5 MB
53. Software Integration/5. Taking a Closer Look at APIs.mp4 115.6 MB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 112.6 MB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 109.0 MB
53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 104.1 MB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 103.4 MB
19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 102.7 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99.3 MB
13. Probability in Other Fields/1. Probability in Finance.mp4 99.1 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp4 97.1 MB
12. Probability Distributions/3. Types of Probability Distributions.mp4 92.3 MB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 92.1 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 89.9 MB
51. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 87.6 MB
58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 86.9 MB
36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 86.5 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 81.1 MB
58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 80.0 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 79.9 MB
12. Probability Distributions/15. Characteristics of Continuous Distributions.mp4 79.8 MB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 79.6 MB
18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 78.2 MB
13. Probability in Other Fields/2. Probability in Statistics.mp4 77.3 MB
51. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 76.3 MB
9. Part 2 Probability/3. Computing Expected Values.mp4 75.7 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 75.5 MB
22. Part 4 Introduction to Python/3. Why Python.mp4 75.1 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 74.6 MB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 74.5 MB
53. Software Integration/9. Software Integration - Explained.mp4 72.6 MB
15. Statistics - Descriptive Statistics/1. Types of Data.mp4 72.5 MB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 71.5 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 71.2 MB
18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4 70.5 MB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 69.5 MB
53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 69.0 MB
58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 68.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 67.7 MB
12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.mp4 65.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64.5 MB
13. Probability in Other Fields/3. Probability in Data Science.mp4 63.5 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 63.1 MB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 62.9 MB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 62.8 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 61.9 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 61.8 MB
9. Part 2 Probability/5. Frequency.mp4 61.7 MB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 61.6 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61.1 MB
53. Software Integration/7. Communication between Software Products through Text Files.mp4 60.3 MB
11. Bayesian Inference/20. Bayes' Law.mp4 59.6 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 59.4 MB
9. Part 2 Probability/7. Events and Their Complements.mp4 59.2 MB
18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4 59.2 MB
52. Deep Learning - Conclusion/3. An overview of CNNs.mp4 58.8 MB
22. Part 4 Introduction to Python/1. Introduction to Programming.mp4 58.5 MB
12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.mp4 58.4 MB
14. Part 3 Statistics/1. Population and Sample.mp4 58.1 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp4 57.9 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp4 57.4 MB
10. Combinatorics/11. Solving Combinations.mp4 57.4 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 57.3 MB
11. Bayesian Inference/7. Union of Sets.mp4 57.2 MB
18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4 57.0 MB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 56.4 MB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 56.1 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp4 56.0 MB
20. Statistics - Hypothesis Testing/10. p-value.mp4 55.9 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp4 55.7 MB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 55.6 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp4 54.8 MB
15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54.4 MB
57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 54.3 MB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54.2 MB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 53.6 MB
37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53.4 MB
51. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 53.1 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4 52.8 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4 52.4 MB
54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 52.3 MB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 51.8 MB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4 51.0 MB
49. Deep Learning - Preprocessing/3. Standardization.mp4 51.0 MB
15. Statistics - Descriptive Statistics/22. Variance.mp4 51.0 MB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50.4 MB
18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 50.0 MB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 49.9 MB
40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4 49.8 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp4 49.7 MB
40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 49.4 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 49.2 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 49.1 MB
12. Probability Distributions/17. Continuous Distributions The Normal Distribution.mp4 48.2 MB
12. Probability Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4 47.9 MB
17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 47.8 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 47.8 MB
44. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 47.7 MB
12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.mp4 47.1 MB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 46.7 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp4 46.0 MB
11. Bayesian Inference/13. The Conditional Probability Formula.mp4 45.9 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4 45.8 MB
11. Bayesian Inference/3. Ways Sets Can Interact.mp4 45.4 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 45.4 MB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 45.1 MB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45.1 MB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 44.8 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp4 44.6 MB
39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 44.6 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp4 44.6 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 44.5 MB
22. Part 4 Introduction to Python/5. Why Jupyter.mp4 44.3 MB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 44.1 MB
9. Part 2 Probability/1. The Basic Probability Formula.mp4 44.0 MB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 43.9 MB
50. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 43.9 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 43.9 MB
10. Combinatorics/9. Solving Variations without Repetition.mp4 43.1 MB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 43.0 MB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 42.9 MB
11. Bayesian Inference/18. The Multiplication Law.mp4 42.9 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 42.8 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp4 42.7 MB
12. Probability Distributions/1. Fundamentals of Probability Distributions.mp4 42.4 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4 41.6 MB
51. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 41.5 MB
10. Combinatorics/3. Permutations and How to Use Them.mp4 41.5 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 41.2 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp4 41.0 MB
10. Combinatorics/19. A Recap of Combinatorics.mp4 40.9 MB
54. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 40.9 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp4 40.6 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 40.6 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 40.4 MB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40.2 MB
12. Probability Distributions/25. Continuous Distributions The Exponential Distribution.mp4 40.2 MB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 39.8 MB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 39.8 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 39.6 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 39.6 MB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39.4 MB
51. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 39.4 MB
10. Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 39.4 MB
54. Case Study - What's Next in the Course/2. The Business Task.mp4 39.2 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 39.1 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4 38.9 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 38.7 MB
10. Combinatorics/13. Symmetry of Combinations.mp4 38.7 MB
44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38.5 MB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38.5 MB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 38.4 MB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38.3 MB
40. Part 6 Mathematics/13. Transpose of a Matrix.mp4 38.1 MB
38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 37.7 MB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 37.7 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 37.5 MB
44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 37.4 MB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37.2 MB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 37.1 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 36.8 MB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4 36.4 MB
51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 36.4 MB
37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36.1 MB
10. Combinatorics/5. Simple Operations with Factorials.mp4 36.1 MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 35.8 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp4 35.7 MB
18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4 35.4 MB
11. Bayesian Inference/15. The Law of Total Probability.mp4 35.2 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 34.9 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 34.9 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 34.9 MB
11. Bayesian Inference/11. Dependence and Independence of Sets.mp4 34.8 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp4 34.8 MB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 34.7 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4 34.5 MB
12. Probability Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4 34.1 MB
10. Combinatorics/7. Solving Variations with Repetition.mp4 34.0 MB
40. Part 6 Mathematics/3. Scalars and Vectors.mp4 33.8 MB
30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 33.6 MB
40. Part 6 Mathematics/1. What is a matrix.mp4 33.6 MB
26. Python - Conditional Statements/4. The ELIF Statement.mp4 33.2 MB
10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 33.1 MB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 32.9 MB
46. Deep Learning - Overfitting/3. What is Validation.mp4 32.7 MB
40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4 32.6 MB
44. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32.5 MB
36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 32.3 MB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 32.3 MB
18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4 32.2 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 32.0 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp4 31.5 MB
41. Part 7 Deep Learning/1. What to Expect from this Part.mp4 31.1 MB
46. Deep Learning - Overfitting/1. What is Overfitting.mp4 31.1 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp4 30.9 MB
28. Python - Sequences/5. List Slicing.mp4 30.8 MB
23. Python - Variables and Data Types/5. Python Strings.mp4 30.8 MB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 30.6 MB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 30.5 MB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 30.1 MB
25. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30.1 MB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 30.0 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 29.9 MB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 29.6 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 29.5 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 29.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 29.5 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 29.5 MB
15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 29.4 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 29.1 MB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29.1 MB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 29.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4 28.8 MB
42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 28.7 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp4 28.7 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 28.7 MB
42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28.4 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp4 28.3 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 28.0 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 27.8 MB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 27.8 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 27.7 MB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4 27.5 MB
15. Statistics - Descriptive Statistics/27. Covariance.mp4 27.5 MB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27.3 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp4 27.2 MB
12. Probability Distributions/21. Continuous Distributions The Students' T Distribution.mp4 27.2 MB
36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27.1 MB
11. Bayesian Inference/5. Intersection of Sets.mp4 27.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4 26.8 MB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 26.7 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 26.4 MB
12. Probability Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4 26.3 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 26.0 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 25.9 MB
50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 25.9 MB
15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 25.9 MB
11. Bayesian Inference/16. The Additive Rule.mp4 25.7 MB
51. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 25.7 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 25.7 MB
57. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 25.5 MB
11. Bayesian Inference/9. Mutually Exclusive Sets.mp4 25.4 MB
23. Python - Variables and Data Types/1. Variables.mp4 25.3 MB
52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25.3 MB
46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 25.2 MB
42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 25.1 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 25.1 MB
46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 25.1 MB
28. Python - Sequences/7. Dictionaries.mp4 25.0 MB
11. Bayesian Inference/1. Sets and Events.mp4 25.0 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp4 24.7 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 24.4 MB
12. Probability Distributions/7. Discrete Distributions The Uniform Distribution.mp4 24.4 MB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 24.2 MB
40. Part 6 Mathematics/14. Dot Product.mp4 24.0 MB
27. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 23.9 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp4 23.7 MB
42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23.3 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4 23.1 MB
36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 23.1 MB
17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4 22.8 MB
12. Probability Distributions/5. Characteristics of Discrete Distributions.mp4 22.7 MB
42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 22.6 MB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 22.6 MB
40. Part 6 Mathematics/8. What is a Tensor.mp4 22.5 MB
17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 22.5 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 22.4 MB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 22.3 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 22.0 MB
27. Python - Python Functions/7. Built-in Functions in Python.mp4 22.0 MB
28. Python - Sequences/1. Lists.mp4 22.0 MB
28. Python - Sequences/3. Using Methods.mp4 22.0 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp4 21.8 MB
47. Deep Learning - Initialization/1. What is Initialization.mp4 21.8 MB
55. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 21.6 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp4 21.5 MB
38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4 21.2 MB
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34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp4 20.1 MB
30. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 19.9 MB
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50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.vtt 8.9 KB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.srt 8.8 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.vtt 8.8 KB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.srt 8.8 KB
58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.vtt 8.7 KB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.srt 8.7 KB
13. Probability in Other Fields/1. Probability in Finance.vtt 8.7 KB
18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.vtt 8.6 KB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.srt 8.6 KB
12. Probability Distributions/15. Characteristics of Continuous Distributions.srt 8.6 KB
53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.srt 8.5 KB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.srt 8.5 KB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.srt 8.5 KB
13. Probability in Other Fields/2. Probability in Statistics.srt 8.4 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.srt 8.4 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.srt 8.4 KB
58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.vtt 8.4 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.vtt 8.4 KB
12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.srt 8.3 KB
12. Probability Distributions/3. Types of Probability Distributions.vtt 8.3 KB
36. Advanced Statistical Methods - Logistic Regression/16.3 Bank_data_testing.csv.csv 8.3 KB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.vtt 8.3 KB
38. Advanced Statistical Methods - K-Means Clustering/3.2 Countries_exercise.csv.csv 8.3 KB
38. Advanced Statistical Methods - K-Means Clustering/7.2 Countries_exercise.csv.csv 8.3 KB
40. Part 6 Mathematics/15. Dot Product of Matrices.vtt 8.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.srt 8.2 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.srt 8.2 KB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.srt 8.1 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.srt 8.1 KB
18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.srt 8.0 KB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).srt 8.0 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.srt 8.0 KB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).vtt 8.0 KB
44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.srt 7.9 KB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.vtt 7.9 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.srt 7.9 KB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 7.9 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.srt 7.9 KB
9. Part 2 Probability/1. The Basic Probability Formula.vtt 7.8 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.srt 7.8 KB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.srt 7.8 KB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.vtt 7.8 KB
51. Deep Learning - Business Case Example/6. Creating a Data Provider.srt 7.8 KB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.vtt 7.8 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).srt 7.7 KB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.vtt 7.7 KB
12. Probability Distributions/15. Characteristics of Continuous Distributions.vtt 7.6 KB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.vtt 7.6 KB
53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.vtt 7.6 KB
12. Probability Distributions/1. Fundamentals of Probability Distributions.srt 7.5 KB
15. Statistics - Descriptive Statistics/22. Variance.srt 7.5 KB
57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.srt 7.5 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.srt 7.5 KB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).srt 7.5 KB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.srt 7.5 KB
13. Probability in Other Fields/2. Probability in Statistics.vtt 7.5 KB
23. Python - Variables and Data Types/5. Python Strings.srt 7.5 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.vtt 7.4 KB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.vtt 7.4 KB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.vtt 7.4 KB
12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.vtt 7.4 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.srt 7.4 KB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.srt 7.4 KB
44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt 7.4 KB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.srt 7.4 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.vtt 7.4 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.srt 7.3 KB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.srt 7.3 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.srt 7.3 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.srt 7.3 KB
58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.srt 7.2 KB
11. Bayesian Inference/20. Bayes' Law.srt 7.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.vtt 7.1 KB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.vtt 7.1 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.vtt 7.1 KB
18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.vtt 7.1 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.vtt 7.1 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.srt 7.1 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.srt 7.0 KB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).vtt 7.0 KB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.srt 7.0 KB
22. Part 4 Introduction to Python/3. Why Python.srt 7.0 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.vtt 6.9 KB
51. Deep Learning - Business Case Example/7. Business Case Model Outline.srt 6.9 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.vtt 6.9 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.vtt 6.9 KB
22. Part 4 Introduction to Python/1. Introduction to Programming.srt 6.9 KB
44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.vtt 6.9 KB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.srt 6.9 KB
53. Software Integration/9. Software Integration - Explained.srt 6.8 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.vtt 6.8 KB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.vtt 6.8 KB
51. Deep Learning - Business Case Example/6. Creating a Data Provider.vtt 6.8 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).srt 6.8 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).vtt 6.8 KB
12. Probability Distributions/1. Fundamentals of Probability Distributions.vtt 6.7 KB
9. Part 2 Probability/7. Events and Their Complements.srt 6.7 KB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.srt 6.7 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt 6.7 KB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.srt 6.7 KB
9. Part 2 Probability/3. Computing Expected Values.srt 6.7 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.srt 6.7 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).srt 6.7 KB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.srt 6.7 KB
26. Python - Conditional Statements/4. The ELIF Statement.srt 6.7 KB
13. Probability in Other Fields/3. Probability in Data Science.srt 6.7 KB
15. Statistics - Descriptive Statistics/22. Variance.vtt 6.6 KB
57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.vtt 6.6 KB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.srt 6.6 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.srt 6.6 KB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.vtt 6.6 KB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.srt 6.6 KB
51. Deep Learning - Business Case Example/8. Business Case Optimization.srt 6.6 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.srt 6.6 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.vtt 6.6 KB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.srt 6.6 KB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).vtt 6.5 KB
12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.srt 6.5 KB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.srt 6.5 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.srt 6.5 KB
44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 6.5 KB
23. Python - Variables and Data Types/5. Python Strings.vtt 6.5 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.vtt 6.4 KB
52. Deep Learning - Conclusion/3. An overview of CNNs.srt 6.4 KB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.srt 6.4 KB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.vtt 6.4 KB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.vtt 6.4 KB
9. Part 2 Probability/5. Frequency.srt 6.4 KB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.vtt 6.4 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.vtt 6.4 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.vtt 6.4 KB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.srt 6.4 KB
11. Bayesian Inference/20. Bayes' Law.vtt 6.4 KB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt 6.4 KB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.srt 6.3 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.vtt 6.3 KB
58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.vtt 6.3 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.srt 6.3 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.srt 6.3 KB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.srt 6.3 KB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.srt 6.2 KB
36. Advanced Statistical Methods - Logistic Regression/5.2 Example_bank_data.csv.csv 6.2 KB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.vtt 6.2 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.vtt 6.2 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.vtt 6.1 KB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.srt 6.1 KB
18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.srt 6.1 KB
22. Part 4 Introduction to Python/3. Why Python.vtt 6.1 KB
30. Python - Advanced Python Tools/1. Object Oriented Programming.srt 6.1 KB
22. Part 4 Introduction to Python/1. Introduction to Programming.vtt 6.1 KB
18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).srt 6.1 KB
51. Deep Learning - Business Case Example/7. Business Case Model Outline.vtt 6.1 KB
23. Python - Variables and Data Types/1. Variables.srt 6.1 KB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.vtt 6.0 KB
53. Software Integration/9. Software Integration - Explained.vtt 6.0 KB
49. Deep Learning - Preprocessing/3. Standardization.srt 6.0 KB
9. Part 2 Probability/7. Events and Their Complements.vtt 6.0 KB
15. Statistics - Descriptive Statistics/1. Types of Data.srt 6.0 KB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt 5.9 KB
53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.srt 5.9 KB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.srt 5.9 KB
9. Part 2 Probability/3. Computing Expected Values.vtt 5.9 KB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.srt 5.9 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).vtt 5.9 KB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.vtt 5.9 KB
13. Probability in Other Fields/3. Probability in Data Science.vtt 5.9 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt 5.9 KB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.srt 5.9 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.srt 5.9 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).vtt 5.8 KB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.vtt 5.8 KB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.vtt 5.8 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.vtt 5.8 KB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.srt 5.8 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.vtt 5.8 KB
25. Python - Other Python Operators/3. Logical and Identity Operators.srt 5.8 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.vtt 5.8 KB
12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.vtt 5.8 KB
51. Deep Learning - Business Case Example/8. Business Case Optimization.vtt 5.8 KB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.vtt 5.8 KB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.vtt 5.8 KB
26. Python - Conditional Statements/4. The ELIF Statement.vtt 5.8 KB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.srt 5.7 KB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.srt 5.7 KB
18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.srt 5.7 KB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.vtt 5.7 KB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.vtt 5.7 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.srt 5.7 KB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.srt 5.7 KB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.srt 5.7 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.vtt 5.7 KB
9. Part 2 Probability/5. Frequency.vtt 5.7 KB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.vtt 5.7 KB
52. Deep Learning - Conclusion/3. An overview of CNNs.vtt 5.7 KB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.vtt 5.7 KB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.srt 5.6 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.srt 5.6 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.srt 5.6 KB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.vtt 5.6 KB
10. Combinatorics/11. Solving Combinations.srt 5.6 KB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.vtt 5.6 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.srt 5.6 KB
46. Deep Learning - Overfitting/1. What is Overfitting.srt 5.6 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.srt 5.6 KB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.srt 5.6 KB
28. Python - Sequences/5. List Slicing.srt 5.6 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.vtt 5.5 KB
11. Bayesian Inference/7. Union of Sets.srt 5.5 KB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).srt 5.5 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.vtt 5.5 KB
53. Software Integration/7. Communication between Software Products through Text Files.srt 5.5 KB
14. Part 3 Statistics/1. Population and Sample.srt 5.5 KB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.vtt 5.5 KB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.srt 5.5 KB
54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt 5.5 KB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.vtt 5.4 KB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.srt 5.4 KB
18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.srt 5.4 KB
18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.vtt 5.4 KB
40. Part 6 Mathematics/13. Transpose of a Matrix.srt 5.4 KB
30. Python - Advanced Python Tools/1. Object Oriented Programming.vtt 5.3 KB
18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).vtt 5.3 KB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.vtt 5.3 KB
51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.srt 5.3 KB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.srt 5.3 KB
49. Deep Learning - Preprocessing/3. Standardization.vtt 5.3 KB
12. Probability Distributions/19. Continuous Distributions The Standard Normal Distribution.srt 5.3 KB
23. Python - Variables and Data Types/1. Variables.vtt 5.3 KB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.srt 5.3 KB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.srt 5.3 KB
15. Statistics - Descriptive Statistics/1. Types of Data.vtt 5.2 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.srt 5.2 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.srt 5.2 KB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.srt 5.2 KB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.srt 5.2 KB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt 5.2 KB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt 5.2 KB
44. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.srt 5.2 KB
53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.vtt 5.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.srt 5.2 KB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.vtt 5.2 KB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).srt 5.1 KB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.vtt 5.1 KB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.srt 5.1 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.vtt 5.1 KB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.vtt 5.1 KB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.srt 5.1 KB
1. Part 1 Introduction/2. What Does the Course Cover.srt 5.1 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.srt 5.1 KB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.srt 5.1 KB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.vtt 5.1 KB
11. Bayesian Inference/1. Sets and Events.srt 5.1 KB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.vtt 5.0 KB
20. Statistics - Hypothesis Testing/10. p-value.srt 5.0 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt 5.0 KB
12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.srt 5.0 KB
18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.vtt 5.0 KB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.vtt 5.0 KB
25. Python - Other Python Operators/3. Logical and Identity Operators.vtt 5.0 KB
28. Python - Sequences/1. Lists.srt 5.0 KB
55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt 5.0 KB
10. Combinatorics/11. Solving Combinations.vtt 5.0 KB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.srt 5.0 KB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.vtt 5.0 KB
11. Bayesian Inference/7. Union of Sets.vtt 5.0 KB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.vtt 5.0 KB
11. Bayesian Inference/13. The Conditional Probability Formula.srt 4.9 KB
46. Deep Learning - Overfitting/1. What is Overfitting.vtt 4.9 KB
15. Statistics - Descriptive Statistics/27. Covariance.srt 4.9 KB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.srt 4.9 KB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.srt 4.9 KB
46. Deep Learning - Overfitting/3. What is Validation.srt 4.9 KB
56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.vtt 4.9 KB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.vtt 4.9 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.vtt 4.9 KB
32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.vtt 4.9 KB
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53. Software Integration/7. Communication between Software Products through Text Files.vtt 4.8 KB
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15. Statistics - Descriptive Statistics/30. Correlation Coefficient.srt 4.7 KB
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7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.srt 4.5 KB
1. Part 1 Introduction/2. What Does the Course Cover.vtt 4.5 KB
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12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.vtt 4.5 KB
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11. Bayesian Inference/3. Ways Sets Can Interact.srt 4.4 KB
11. Bayesian Inference/13. The Conditional Probability Formula.vtt 4.4 KB
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32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.srt 4.2 KB
30. Python - Advanced Python Tools/7. Importing Modules in Python.vtt 4.2 KB
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18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.srt 4.1 KB
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58. Case Study - Analyzing the Predicted Outputs in Tableau/3. EXERCISE - Reasons vs Probability.html 401 bytes
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53. Software Integration/10. Software Integration - Explained.html 158 bytes
53. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html 158 bytes
53. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html 158 bytes
53. Software Integration/6. Taking a Closer Look at APIs.html 158 bytes
53. Software Integration/8. Communication between Software Products through Text Files.html 158 bytes
54. Case Study - What's Next in the Course/4. Introducing the Data Set.html 158 bytes
6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html 158 bytes
7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html 158 bytes
8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html 158 bytes
9. Part 2 Probability/2. The Basic Probability Formula.html 158 bytes
9. Part 2 Probability/4. Computing Expected Values.html 158 bytes
9. Part 2 Probability/6. Frequency.html 158 bytes
9. Part 2 Probability/8. Events and Their Complements.html 158 bytes
50. Deep Learning - Classifying on the MNIST Dataset/11.2 TensorFlow MNIST 'Around 98% Accuracy' Solution.html 157 bytes
44. Deep Learning - TensorFlow Introduction/8.1 Basic NN Example with TensorFlow (Complete).html 156 bytes
55. 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/5.2 Basic NN Example Exercise 3b Solution.html 154 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Basic NN Example Exercise 3a 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
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Basic NN Example Exercise 3c Solution.html 154 bytes
44. Deep Learning - TensorFlow Introduction/5.1 Basic NN Example with TensorFlow (Part 1).html 154 bytes
44. Deep Learning - TensorFlow Introduction/6.1 Basic NN Example with TensorFlow (Part 2).html 154 bytes
44. Deep Learning - TensorFlow Introduction/7.1 Basic NN Example with TensorFlow (Part 3).html 154 bytes
44. Deep Learning - TensorFlow Introduction/9.8 Basic NN Example with TensorFlow (All Exercises).html 154 bytes
50. Deep Learning - Classifying on the MNIST Dataset/9.1 TensorFlow MNIST Complete Code with Comments.html 152 bytes
50. Deep Learning - Classifying on the MNIST Dataset/11.3 TensorFlow MNIST '2. Depth' Solution.html 150 bytes
50. Deep Learning - Classifying on the MNIST Dataset/11.7 TensorFlow MNIST '1. Width' Solution.html 150 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Basic NN Example Exercise 6 Solution.html 149 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Basic NN Example Exercise 5 Solution.html 149 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Basic NN Example Exercise 2 Solution.html 149 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 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 4 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
50. Deep Learning - Classifying on the MNIST Dataset/10.1 TensorFlow MNIST All Exercises.html 144 bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Basic NN Example (All Exercises).html 143 bytes
55. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html 142 bytes
55. 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. Part 1 Introduction/3.2 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
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18.1 Dealing with categorical data.html 134 bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/19.1 Dealing with categorical data.html 134 bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20.1 Making predictions.html 134 bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3.1 Adjusted R-squared.html 134 bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/5.1 Multiple linear regression - exercise.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/10.1 Feature selection.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/11.1 Calculation of P-values.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/12.1 Summary table with p-values.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/13.1 Multiple linear regression - Exercise.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/14.1 Feature scaling.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/15.1 Feature scaling standardization.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/16.1 Predicting with the Standardized Cofficients.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/17.1 Feature scaling - exercise.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/19.1 Train - Test split explained.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/6.1 Simple linear regression with sklearn.html 134 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/9.1 Calculating the Adjusted R-Squared.html 134 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/1.1 sklearn - Linear Regression - Practical Example (Part 1).html 134 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/2.1 sklearn - Linear Regression - Practical Example (Part 2).html 134 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/4.1 sklearn - Linear Regression - Practical Example (Part 3).html 134 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/6.1 sklearn - Linear Regression - Practical Example (Part 4).html 134 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/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/11.1 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/16.1 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/15.3 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
51. Deep Learning - Business Case Example/11.1 TensorFlow Business Case Homework.html 134 bytes
51. Deep Learning - Business Case Example/12.1 TensorFlow Business Case Homework.html 134 bytes
51. Deep Learning - Business Case Example/4.1 Audiobooks Preprocessing.html 134 bytes
51. Deep Learning - Business Case Example/5.1 Preprocessing Exercise.html 134 bytes
51. Deep Learning - Business Case Example/6.1 Creating a Data Provider (Class).html 134 bytes
51. Deep Learning - Business Case Example/7.1 TensorFlow Business Case Model Outline.html 134 bytes
51. Deep Learning - Business Case Example/8.1 TensorFlow Business Case Optimization.html 134 bytes
51. Deep Learning - Business Case Example/9.1 TensorFlow Business Case Interpretation.html 134 bytes
57. Case Study - Loading the 'absenteeism_module'/1.1 5 Files Needed to Deploy the Model.html 134 bytes
55. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 bytes
55. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 117 bytes
55. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html 116 bytes
55. 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. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html 81 bytes
18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html 81 bytes
18. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html 81 bytes
18. Statistics - Inferential Statistics Confidence Intervals/4. Confidence Intervals; Population Variance Known; z-score; Exercise.html 81 bytes
18. Statistics - Inferential Statistics Confidence Intervals/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
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/19. Dealing with Categorical Data - Dummy Variables.html 76 bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/5. Multiple Linear Regression Exercise.html 76 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/13. Multiple Linear Regression - Exercise.html 76 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/17. Feature Scaling (Standardization) - Exercise.html 76 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/6. Simple Linear Regression with sklearn - Exercise.html 76 bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/9. Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/5. Dummies and Variance Inflation Factor - Exercise.html 76 bytes

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