Introduction to supervised machine learning
The tools used in this course
Linear models
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Introduction to Linear Regression (3:36)
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Linear Regression in Python (6:20)
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Introduction to Ridge Regression (4:20)
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Ridge Regression in Python (5:34)
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Introduction to Lasso Regression (5:15)
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Lasso Regression in Python (4:34)
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Introduction to Elastic Net Regression (4:43)
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Elastic Net Regression in Python (4:07)
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Introduction to Logistic Regression for classification (7:22)
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Logistic Regression in Python (15:19)
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Student quiz
K-nearest neighbors
Naive Bayes
Support Vector Machines
Neural Networks
Introduction to ensemble models
Ensemble models: bagging
Ensemble models: boosting
Ensemble models: voting
Ensemble models: stacking
Performance evaluation
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Regression performance metrics (10:07)
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Regression performance metrics in Python (5:43)
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Pairplot in Python (5:02)
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Binary classification performance metrics (13:07)
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Binary classification performance metrics in Python (9:49)
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Introduction to ROC curve (7:38)
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ROC curve in Python (5:21)
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Multi-class classification performance metrics (7:58)
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Multi-class classification performance metrics in Python (7:38)
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When to use classification performance metrics (6:05)
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Student quiz
Cross-Validation and hyperparameter tuning
Feature importance and model interpretation
Recursive Feature Elimination
Persisting our model
Practical examples in Python
Practical approaches
End of course
