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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

Ridge Regression Fundamentals Lasso Regression and Sparsity Elastic Net and Mixing Parameter Choosing Regularization Strength Coordinate Descent Algorithms Cross-Validated Regularization Paths Polynomial Regression with Regularization Generalized Additive Models Overview Quantile Regression Applications Poisson and Negative Binomial Regression Robust Regression Techniques Feature Selection via L1 Penalty Bayesian Linear Regression Basics Multitask and Multioutput Regression Nonlinear Regression with Kernels

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/Regression II: Regularization and Advanced Techniques

Regression II: Regularization and Advanced Techniques

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Control complexity and improve generalization using ridge, lasso, elastic net, and specialized regressors.

Content

10 of 15

Poisson and Negative Binomial Regression

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