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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

13Model Tuning, Pipelines, and Experiment Tracking

Grid Search and Random Search Bayesian Optimization Basics Successive Halving and Hyperband Early Stopping and Warm Starts Hyperparameter Spaces and Priors Pipeline Composition and Caching ColumnTransformers for Heterogeneous Data Custom Transformers and Estimators Cross-Validated Pipelines Refit Strategies and Model Persistence Reproducible Experiment Tracking Logging and Metadata Management Parallel and Distributed Tuning Budget-Aware Optimization Reusing and Sharing Artifacts

14Model Interpretability and Responsible AI

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Model Tuning, Pipelines, and Experiment Tracking

Model Tuning, Pipelines, and Experiment Tracking

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Automate workflows, search hyperparameters, and track experiments reproducibly.

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Refit Strategies and Model Persistence

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