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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

11Handling Real-World Data Issues

12Dimensionality Reduction and Feature Selection

Filter Methods for Feature Selection Wrapper Methods and RFE Embedded Methods with Regularization Mutual Information for Supervised Tasks Correlation-Based Feature Pruning Principal Component Analysis PCA for Preprocessing Pipelines Sparse PCA and Kernel PCA Linear Discriminant Analysis t-SNE and UMAP for Exploration Autoencoder Features Overview Variance Thresholding Stability Selection Techniques Feature Selection under Imbalance Interpreting Reduced Dimensions

13Model Tuning, Pipelines, and Experiment Tracking

14Model Interpretability and Responsible AI

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Dimensionality Reduction and Feature Selection

Dimensionality Reduction and Feature Selection

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Reduce redundancy and highlight signal with supervised and unsupervised techniques.

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Autoencoder Features Overview

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