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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

Simple Linear Regression Geometry Multiple Linear Regression Formulation Assumptions and Diagnostics Ordinary Least Squares Solution Gradient Descent for OLS Heteroscedasticity and Robust Losses Transformations of Targets and Features Categorical Variables in Regression Interaction Terms in Linear Models Multicollinearity and VIF Prediction Intervals vs Confidence Intervals Feature Scaling Effects in OLS Handling Outliers with Huber and Quantile Loss Model Interpretation with Coefficients Baseline and Dummy Regressors

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

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/Regression I: Linear Models

Regression I: Linear Models

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Build and diagnose linear regression models, understand assumptions, and evaluate predictive performance.

Content

9 of 15

Interaction Terms in Linear Models

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