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Supervised Machine Learning in Python
Introduction to supervised machine learning
Introduction to the course (4:53)
What is supervised machine learning? (10:31)
Regression and classification models (6:22)
Overfitting and underfitting (15:09)
Student quiz
The tools used in this course
Required Python packages
Jupyter notebook (9:08)
Sklearn API (9:38)
Linear models
Introduction to Linear Regression (3:36)
Linear Regression in Python (6:20)
Introduction to Ridge Regression (4:20)
Ridge Regression in Python (5:34)
Introduction to Lasso Regression (5:15)
Lasso Regression in Python (4:34)
Introduction to Elastic Net Regression (4:43)
Elastic Net Regression in Python (4:07)
Introduction to Logistic Regression for classification (7:22)
Logistic Regression in Python (15:19)
Student quiz
Decision trees
Introduction to decision trees (20:17)
Decision Trees in Python (12:15)
Student quiz
K-nearest neighbors
Introduction to KNN (6:31)
KNN in Python (11:40)
Student quiz
Naive Bayes
Introduction to Naive Bayes (7:07)
Categorical Naive Bayes in Python (6:43)
Bernoulli Naive Bayes in Python (5:09)
Gaussian Naive Bayes in Python (4:22)
Student quiz
Support Vector Machines
Introduction to SVM (6:05)
Linear SVM in Python (7:08)
Non-linear SVM in Python (11:05)
Student quiz
Neural Networks
Introduction to Neural Networks (21:11)
Neural Networks in Python (15:42)
Student quiz
Introduction to ensemble models
Ensemble models and bias-variance tradeoff (5:23)
Ensemble models: bagging
Introduction to bagging (6:49)
Bagging in Python (12:43)
Introduction to Random Forest (2:53)
Random Forest in Python (11:51)
Introduction to Extremely Randomized trees (2:31)
Extremely Randomized trees in Python (10:05)
Student quiz
Ensemble models: boosting
Introduction to Boosting (4:28)
Boosting in Python (8:53)
Introduction to Gradient Boosting (4:40)
Gradient Boosting in Python (9:15)
XGBoost in Python (10:48)
Student quiz
Ensemble models: voting
Introduction to voting (3:43)
Voting in Python (6:25)
Student quiz
Ensemble models: stacking
Introduction to stacking (2:12)
Stacking in Python (8:02)
Student quiz
Performance evaluation
Regression performance metrics (10:07)
Regression performance metrics in Python (5:43)
Pairplot in Python (5:02)
Binary classification performance metrics (13:07)
Binary classification performance metrics in Python (9:49)
Introduction to ROC curve (7:38)
ROC curve in Python (5:21)
Multi-class classification performance metrics (7:58)
Multi-class classification performance metrics in Python (7:38)
When to use classification performance metrics (6:05)
Student quiz
Cross-Validation and hyperparameter tuning
Introduction to k-fold cross-validation (12:25)
k-fold cross-validation in Python (8:23)
The need for hyperparameter tuning (1:19)
Introduction to grid search (4:49)
Grid search in Python (14:13)
Introduction to random search (4:42)
Random search in Python (14:09)
Student quiz
Feature importance and model interpretation
What is feature importance? (8:37)
Models that calculate feature importance in Python (18:15)
Introduction to SHAP (10:14)
Using SHAP with tree-based models in Python (21:53)
Using SHAP with every model in Pyhton (22:14)
Student quiz
Recursive Feature Elimination
Introduction to RFE (6:59)
RFE in Python (17:22)
Student quiz
Persisting our model
Pickle library (8:39)
Practical examples in Python
A complete pipeline: model selection and hyperparameter tuning (19:45)
Feature selection with Lasso (9:12)
Dimensionality reduction using RFE (5:02)
How to choose the right scaler (5:45)
Practical approaches
The curse of dimensionality
The importance of pre-processing
The importance of the right features against the model
Interpretability of a model
End of course
End of course
Introduction to Ridge Regression
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