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Supervised Machine Learning: Regression and Classification

Chapters

1Foundations of Supervised Learning

2Data Wrangling and Feature Engineering

3Exploratory Data Analysis for Predictive Modeling

4Train/Validation/Test and Cross-Validation Strategies

5Regression I: Linear Models

6Regression II: Regularization and Advanced Techniques

7Classification I: Logistic Regression and Probabilistic View

8Classification II: Thresholding, Calibration, and Metrics

9Distance- and Kernel-Based Methods

10Tree-Based Models and Ensembles

Decision Trees for Regression Decision Trees for Classification Impurity and Splitting Criteria Pruning and Regularization of Trees Handling Missing Values in Trees Random Forests Essentials Extremely Randomized Trees Gradient Boosting Fundamentals Learning Rate, Depth, and Estimators XGBoost, LightGBM, and CatBoost Feature Importance and Permutation Partial Dependence and ICE with Trees Handling Imbalanced Data with Ensembles Calibration of Ensemble Predictions Stacking and Blending Strategies

11Handling Real-World Data Issues

12Dimensionality Reduction and Feature Selection

13Model Tuning, Pipelines, and Experiment Tracking

14Model Interpretability and Responsible AI

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Tree-Based Models and Ensembles

Tree-Based Models and Ensembles

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Learn interpretable trees and powerful ensembles like random forests and gradient boosting.

Content

10 of 15

XGBoost, LightGBM, and CatBoost

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