1. Introduction
2. Machine Learning 101
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1. What Is Machine Learning (6:52)
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2. AIMachine LearningData Science (4:51)
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3. Exercise Machine Learning Playground (6:16)
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4. How Did We Get Here (6:03)
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5. Exercise YouTube Recommendation Engine (4:24)
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6. Types of Machine Learning (4:41)
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7. Are You Getting It Yet
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8. What Is Machine Learning Round 2 (4:44)
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9. Section Review (1:48)
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10. Monthly Coding Challenges, Free Resources and Guides
3. Machine Learning and Data Science Framework
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2. Introducing Our Framework (2:38)
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3. 6 Step Machine Learning Framework (4:58)
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3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post)
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4. Types of Machine Learning Problems (10:32)
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5. Types of Data (4:50)
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6. Types of Evaluation (3:31)
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7. Features In Data (5:22)
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8. Modelling - Splitting Data (5:58)
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9. Modelling - Picking the Model (4:35)
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10. Modelling - Tuning (3:17)
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11. Modelling - Comparison (9:32)
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12. Overfitting and Underfitting Definitions
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13. Experimentation (3:35)
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14. Tools We Will Use (3:59)
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15. Optional Elements of AI
4. The 2 Paths
6. Pandas Data Analysis
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1. Section Overview (2:27)
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2. Downloading Workbooks and Assignments
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3. Pandas Introduction (4:29)
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4. Series, Data Frames and CSVs (13:21)
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5. Data from URLs
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6. Describing Data with Pandas (9:48)
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7. Selecting and Viewing Data with Pandas (11:08)
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8. Selecting and Viewing Data with Pandas Part 2 (13:06)
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9. Manipulating Data (13:56)
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10. Manipulating Data 2 (9:56)
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11. Manipulating Data 3 (10:12)
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12. Assignment Pandas Practice
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13. How To Download The Course Assignments (7:43)
7. NumPy
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1. Section Overview (2:40)
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2. NumPy Introduction (5:17)
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3. Quick Note Correction In Next Video
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4. NumPy DataTypes and Attributes (14:05)
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5. Creating NumPy Arrays (9:22)
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6. NumPy Random Seed (7:17)
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7. Viewing Arrays and Matrices (9:35)
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8. Manipulating Arrays (11:31)
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9. Manipulating Arrays 2 (9:44)
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10. Standard Deviation and Variance (7:10)
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11. Reshape and Transpose (7:26)
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12. Dot Product vs Element Wise (11:45)
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13. Exercise Nut Butter Store Sales (13:04)
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14. Comparison Operators (3:33)
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15. Sorting Arrays (6:19)
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16. Turn Images Into NumPy Arrays (7:37)
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17. Assignment NumPy Practice
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18. Optional Extra NumPy resources
8. Matplotlib Plotting and Data Visualization
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1. Section Overview (1:50)
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2. Matplotlib Introduction (5:16)
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3. Importing And Using Matplotlib (11:36)
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4. Anatomy Of A Matplotlib Figure (9:19)
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5. Scatter Plot And Bar Plot (10:09)
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6. Histograms And Subplots (8:40)
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7. Subplots Option 2 (4:15)
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8. Quick Tip Data Visualizations (1:48)
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9. Plotting From Pandas DataFrames (5:58)
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10. Quick Note Regular Expressions
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11. Plotting From Pandas DataFrames 2 (10:33)
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12. Plotting from Pandas DataFrames 3 (8:32)
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13. Plotting from Pandas DataFrames 4 (6:36)
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14. Plotting from Pandas DataFrames 5 (8:28)
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15. Plotting from Pandas DataFrames 6 (8:27)
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16. Plotting from Pandas DataFrames 7 (11:20)
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17. Customizing Your Plots (10:09)
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18. Customizing Your Plots 2 (9:41)
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19. Saving And Sharing Your Plots (4:14)
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20. Assignment Matplotlib Practice
9. Scikit-learn Creating Machine Learning Models
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1. Section Overview (2:29)
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2. Scikit-learn Introduction (6:41)
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3. Quick Note Upcoming Video
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4. Refresher What Is Machine Learning (5:40)
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5. Quick Note Upcoming Videos
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6. Scikit-learn Cheatsheet (6:12)
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7. Typical scikit-learn Workflow (23:14)
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8. Optional Debugging Warnings In Jupyter (18:57)
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9. Getting Your Data Ready Splitting Your Data (8:37)
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10. Quick Tip Clean, Transform, Reduce (5:03)
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11. Getting Your Data Ready Convert Data To Numbers (16:54)
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12. Getting Your Data Ready Handling Missing Values With Pandas (12:22)
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13. Extension Feature Scaling
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14. Note Correction in the upcoming video (splitting data)
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15. Getting Your Data Ready Handling Missing Values With Scikit-learn (17:29)
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16. Choosing The Right Model For Your Data (14:54)
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17. Choosing The Right Model For Your Data 2 (Regression) (8:41)
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18. Quick Note Decision Trees
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19. Quick Tip How ML Algorithms Work (1:25)
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20. Choosing The Right Model For Your Data 3 (Classification) (12:45)
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21. Fitting A Model To The Data (6:45)
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22. Making Predictions With Our Model (8:24)
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23. predict() vs predict_proba() (8:33)
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24. Making Predictions With Our Model (Regression) (6:49)
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25. Evaluating A Machine Learning Model (Score) (8:57)
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26. Evaluating A Machine Learning Model 2 (Cross Validation) (13:15)
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27. Evaluating A Classification Model 1 (Accuracy) (4:46)
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28. Evaluating A Classification Model 2 (ROC Curve) (9:04)
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29. Evaluating A Classification Model 3 (ROC Curve) (7:44)
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30. Reading Extension ROC Curve + AUC
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31. Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
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32. Evaluating A Classification Model 5 (Confusion Matrix) (8:07)
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33. Evaluating A Classification Model 6 (Classification Report) (10:16)
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34. Evaluating A Regression Model 1 (R2 Score) (9:12)
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35. Evaluating A Regression Model 2 (MAE) (4:17)
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36. Evaluating A Regression Model 3 (MSE) (6:34)
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37. Machine Learning Model Evaluation
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38. Evaluating A Model With Cross Validation and Scoring Parameter (14:04)
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39. Evaluating A Model With Scikit-learn Functions (12:14)
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40. Improving A Machine Learning Model (11:16)
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41. Tuning Hyperparameters (23:15)
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42. Tuning Hyperparameters 2 (14:23)
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43. Tuning Hyperparameters 3 (14:59)
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44. Note Metric Comparison Improvement
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45. Quick Tip Correlation Analysis (2:28)
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46. Saving And Loading A Model (7:28)
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47. Saving And Loading A Model 2 (6:20)
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48. Putting It All Together (20:19)
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49. Putting It All Together 2 (11:34)
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50. Scikit-Learn Practice
11. Milestone Project 1 Supervised Learning (Classification)
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1. Section Overview (2:09)
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2. Project Overview (6:09)
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3. Project Environment Setup (10:58)
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4. Optional Windows Project Environment Setup (4:52)
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5. Step 1~4 Framework Setup (12:06)
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6. Getting Our Tools Ready (9:04)
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7. Exploring Our Data (8:33)
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8. Finding Patterns (10:02)
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9. Finding Patterns 2 (16:47)
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10. Finding Patterns 3 (13:36)
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11. Preparing Our Data For Machine Learning (8:51)
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12. Choosing The Right Models (10:15)
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13. Experimenting With Machine Learning Models (6:31)
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14. TuningImproving Our Model (13:49)
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15. Tuning Hyperparameters (11:27)
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16. Tuning Hyperparameters 2 (11:49)
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17. Tuning Hyperparameters 3 (7:06)
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18. Quick Note Confusion Matrix Labels
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19. Evaluating Our Model (10:59)
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20. Evaluating Our Model 2 (5:54)
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21. Evaluating Our Model 3 (8:49)
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22. Finding The Most Important Features (16:07)
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23. Reviewing The Project (9:13)
12. Milestone Project 2 Supervised Learning (Time Series Data)
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1. Section Overview (1:07)
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2. Project Overview (4:24)
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3. Project Environment Setup (10:52)
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4. Step 1~4 Framework Setup (8:36)
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5. Downloading the data for the next two projects
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6. Exploring Our Data (14:16)
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7. Exploring Our Data 2 (6:16)
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8. Feature Engineering (15:24)
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9. Turning Data Into Numbers (15:38)
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10. Filling Missing Numerical Values (12:49)
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11. Filling Missing Categorical Values (8:27)
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12. Fitting A Machine Learning Model (7:16)
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13. Splitting Data (10:00)
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14. Challenge What's wrong with splitting data after filling it
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15. Custom Evaluation Function (11:13)
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16. Reducing Data (10:36)
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17. RandomizedSearchCV (9:32)
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18. Improving Hyperparameters (8:11)
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19. Preproccessing Our Data (13:15)
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20. Making Predictions (9:17)
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21. Feature Importance (13:50)
13. Data Engineering
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1. Data Engineering Introduction (3:23)
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2. What Is Data (6:42)
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3. What Is A Data Engineer (4:20)
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4. What Is A Data Engineer 2 (5:35)
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5. What Is A Data Engineer 3 (5:03)
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6. What Is A Data Engineer 4 (3:22)
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7. Types Of Databases (6:50)
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8. Quick Note Upcoming Video
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9. Optional OLTP Databases (10:54)
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10. Optional Learn SQL
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11. Hadoop, HDFS and MapReduce (4:22)
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12. Apache Spark and Apache Flink (2:07)
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13. Kafka and Stream Processing (4:33)
15. Storytelling + Communication How To Present Your Work
18. Learn Python Part 2
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1. Breaking The Flow (2:34)
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2. Conditional Logic (13:17)
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3. Indentation In Python (4:38)
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4. Truthy vs Falsey (5:17)
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5. Ternary Operator (4:14)
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6. Short Circuiting (4:02)
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7. Logical Operators (6:56)
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8. Exercise Logical Operators (7:47)
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9. is vs == (7:36)
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10. For Loops (7:01)
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11. Iterables (6:43)
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12. Exercise Tricky Counter (3:23)
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13. range() (5:38)
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14. enumerate() (4:37)
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15. While Loops (6:28)
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16. While Loops 2 (5:49)
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17. break, continue, pass (4:15)
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18. Our First GUI (8:48)
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19. DEVELOPER FUNDAMENTALS IV (6:34)
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20. Exercise Find Duplicates (3:54)
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21. Functions (7:41)
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22. Parameters and Arguments (4:24)
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23. Default Parameters and Keyword Arguments (5:40)
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24. return (13:11)
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25. Exercise Tesla
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26. Methods vs Functions (4:33)
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27. Docstrings (3:47)
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28. Clean Code (4:38)
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29. args and kwargs (7:56)
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30. Exercise Functions (4:18)
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31. Scope (3:37)
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32. Scope Rules (6:55)
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33. global Keyword (6:13)
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34. nonlocal Keyword (3:20)
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35. Why Do We Need Scope (3:38)
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36. Pure Functions (9:23)
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37. map() (6:30)
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38. filter() (4:23)
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39. zip() (3:28)
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40. reduce() (7:31)
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41. List Comprehensions (8:37)
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42. Set Comprehensions (6:26)
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43. Exercise Comprehensions (4:36)
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44. Python Exam Testing Your Understanding
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45. Modules in Python (10:54)
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46. Quick Note Upcoming Videos
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47. Optional PyCharm (8:19)
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48. Packages in Python (10:45)
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49. Different Ways To Import (7:03)
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50. Next Steps
19. Bonus Learn Advanced Statistics and Mathematics for FREE!
21. BONUS SECTION
