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Supervised Machine Learning: Regression and Classification

Chapters

1Foundations of Supervised Learning

Supervised vs Unsupervised vs Reinforcement Inputs, Targets, and Hypothesis Space Bias–Variance Trade-off Underfitting and Overfitting Empirical Risk Minimization Loss Functions Overview Probabilistic Perspective of Supervised Learning Optimization Basics for ML Gradient Descent and Variants Stochasticity and Mini-batching Evaluation vs Training Objectives Data Leakage Pitfalls Reproducibility and Random Seeds Problem Framing: Regression vs Classification Types of Supervision and Labels

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

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/Foundations of Supervised Learning

Foundations of Supervised Learning

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Core concepts, goals, trade-offs, and terminology that underpin regression and classification.

Content

14 of 15

Problem Framing: Regression vs Classification

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