"Machine Learning with Python: Zero to GBMs" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python.
Lesson 1 - Linear Regression with Scikit Learn : Preparing data for machine learning, Linear regression with multiple features, Generating predictions and evaluating models
Lesson 2 - Logistic Regression for ClassificationPreview : Training a logistic regression model, Model evaluation, prediction & persistence
Lesson 3 - Decision Trees and HyperparametersPreview : Downloading and Preparing a real world dataset for training, Training & interpreting decision trees
Lesson 4 - Random Forests and RegularizationPreview : Training and interpreting random forests, Ensemble methods and random forests, Hyperparameter tuning and regularization
Lesson 5 - Gradient Boosting with XGBoostPreview : Training and evaluating a XGBoost model, Data normalization and cross-validation, Hyperparameter tuning and regularization
Lesson 6 - Unsupervised Learning and RecommendationsPreview : Clustering and dimensionality reduction, Collaborative filtering and recommendations, Other supervised learning algorithms