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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

Holdout Validation Principles K-Fold Cross-Validation Stratified K-Fold for Classification Grouped and Blocked CV Time Series Split Strategies Nested Cross-Validation Repeated CV and ShuffleSplit Leakage-Free Preprocessing within CV Evaluating Variance of Estimates Confidence Intervals via Bootstrapping Model Selection vs Model Assessment Early Stopping with Validation Curves Learning Curves Interpretation Data Snooping and Multiple Testing Cross-Validation for Imbalanced Data

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/Train/Validation/Test and Cross-Validation Strategies

Train/Validation/Test and Cross-Validation Strategies

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Design robust evaluation schemes and prevent leakage with correct resampling and learning curves.

Content

11 of 15

Model Selection vs Model Assessment

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