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AI: Advanced Machine Learning - Take Your Machine Learning Skills To The Next Level
Section 1 Welcome to Advanced Machine Learning
Lecture 1 Welcome (3:28)
Lecture 2 Prerequisites (2:13)
Lecture 3 The Classifier Interface (8:29)
Lecture 4 The Regressor Interface (2:53)
Lecture 5 The Transformer Interface (2:11)
Lecture 6 The Cluster Interface (6:04)
Lecture 7 The Manifold Interface (3:32)
Lecture 8 scikitLearn Interface Summary (4:01)
Lecture 9 CrossValidation With Cross_Val_Score (6:18)
Lecture 10 Parameter Searches With GridSearchCV (6:13)
Section 2 Model Complexity, Overfitting And Underfitting
Lecture 11 What Is Model Complexity And Overfitting (2:58)
Lecture 12 Linear Models InDepth (11:05)
Lecture 13 Kernel SVMs InDepth (7:40)
Lecture 14 Random Forests InDepth (6:02)
Lecture 15 Learning Curves For Analyzing Model Complexity (3:53)
Lecture 16 Validation Curves For Analyzing Model Parameters (2:30)
Lecture 17 Efficient Parameter Search With EstimatorCV Objects (5:12)
Section 3 Pipelines
Lecture 18 Motivation Of Using Pipelines (3:09)
Lecture 19 Defining A Pipeline And Basic Usage (6:29)
Lecture 20 CrossValidation With Pipelines (2:31)
Lecture 21 Parameter Selection With Pipelines (4:36)
Section 4 Advanced Metrics And Imbalanced Classes
Lecture 22 Be Mindful Of Default Metrics (7:04)
Lecture 23 More Evaluation Methods For Classification (5:16)
Lecture 24 AUC (6:45)
Lecture 25 Defining Custom Metrics (5:38)
Section 5 Model Selection For Unsupervised Learning
Lecture 26 Guidelines For Unsupervised Model Selection (6:52)
Lecture 27 Model Selection For Density Models (5:53)
Lecture 28 Model Selection For Clustering (4:44)
Section 6 Dealing With Categorical Variables, Dictionaries, And Incomplete Data
Lecture 29 Why Real Data Is Messy (6:23)
Lecture 30 OneHot Encoding For Categorical Data (6:24)
Lecture 31 Working With Dictionaries (2:01)
Lecture 32 Handling Incomplete Data (4:15)
Section 7 Handling Text Data
Lecture 33 Motivation (2:51)
Lecture 34 BagOfWords Representations (6:48)
Lecture 35 Text Classification For Sentiment Analysis Part 1 (7:25)
Lecture 36 Text Classification For Sentiment Analysis Part 2 (4:00)
Lecture 37 The Hashing Trick (3:25)
Lecture 38 Other Representations Distributed Word Representations (2:38)
Section 8 Out Of Core Learning
Lecture 39 The TradeOffs Of Out Of Core Learning (4:43)
Lecture 40 The scikitLearn Interface For Out Of Core Learning (5:12)
Lecture 41 Kernel Approximations For LargeScale NonLinear Classification (5:06)
Lecture 42 Subsample And Transform Supervised Transformations For Out Of Core Learning (5:35)
Lecture 43 Application OutOfCore Text Classification (4:57)
Section 9 Outro
Lecture 44 Conclusion (3:29)
Lecture 45 Final Words (3:14)
Working Files
Working Files
Lecture 15 Learning Curves For Analyzing Model Complexity
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