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Supervised Machine Learning: Regression and Classification

Chapters

1Foundations of Supervised Learning

2Data Wrangling and Feature Engineering

Data Types and Tidy Structure Handling Missing Values Outlier Detection and Treatment Categorical Encoding Schemes Ordinal vs Nominal Encodings Text Features: Bag-of-Words and TF-IDF Date and Time Feature Extraction Scaling and Normalization Techniques Binning and Discretization Interaction and Polynomial Features Target Leakage in Feature Engineering Feature Creation from Domain Knowledge Sparse vs Dense Representations Feature Hashing Basics Managing High Cardinality

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

14Model Interpretability and Responsible AI

15Deployment, Monitoring, and Capstone Project

Courses/Supervised Machine Learning: Regression and Classification/Data Wrangling and Feature Engineering

Data Wrangling and Feature Engineering

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Practical techniques to clean, encode, scale, and construct informative features while avoiding leakage.

Content

14 of 15

Feature Hashing Basics

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