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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

k-Nearest Neighbors for Regression k-Nearest Neighbors for Classification Distance Metrics and Scaling Effects Curse of Dimensionality Efficient Neighbor Search Structures Kernel Trick Intuition SVM for Classification SVM for Regression (SVR)Linear vs Nonlinear Kernels Hyperparameters C, gamma, epsilon Margin Maximization and Slack Variables Feature Mapping vs Implicit Kernels Handling Nonseparable Data Class Weights in SVMs Probabilistic Outputs for SVM

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/Distance- and Kernel-Based Methods

Distance- and Kernel-Based Methods

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Leverage neighborhood and kernel ideas with kNN and SVM for nonlinear decision boundaries.

Content

12 of 15

Feature Mapping vs Implicit Kernels

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