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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

Noisy Labels and Annotation Quality Out-of-Distribution Detection Data Leakage from Temporal Effects Drift Detection and Adaptation Rare Events and Positive-Unlabeled Data High Cardinality Categorical Features Skewed Targets in Regression Missing Not at Random Mechanisms Data Augmentation for Tabular Data Weak Supervision and Distant Labels Semi-Supervised Add-ons to Supervised Privacy-Preserving Feature Engineering Federated Learning Basics for Supervised Small Data and High-D Variants Shortcut Learning and Spurious Correlation

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/Handling Real-World Data Issues

Handling Real-World Data Issues

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Tackle noise, drift, imbalance, and other practical dataset challenges in production-like settings.

Content

14 of 15

Small Data and High-D Variants

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