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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

Univariate Distributions and Summary Stats Pairwise Relationships and Correlations Visualization for Regression Targets Visualization for Class Imbalance Detecting Nonlinearity and Heteroscedasticity Multicollinearity Diagnostics Train–Test Split Before EDA Stratification Strategies Leakage-Aware EDA Practices Robust Scaling Decisions from EDA Identifying Data Quality Issues Feature Importance via Baseline Models Partial Plots for Early Insight Handling Out-of-Range Values Data Imputation Strategy Design

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

14Model Interpretability and Responsible AI

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Exploratory Data Analysis for Predictive Modeling

Exploratory Data Analysis for Predictive Modeling

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EDA methods tailored to supervised tasks to reveal signal, distribution shifts, and modeling risks.

Content

15 of 15

Data Imputation Strategy Design

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