Logistic Regression

Summary

A linear model for binary classification that outputs a probability via the sigmoid function — simple, interpretable, and still a strong baseline for classification tasks.

How to Learn This

1Implement logistic regression with scikit-learn on a binary classification dataset.
2Learn to interpret coefficients as log-odds.
3Understand the decision boundary logistic regression draws and where it fails (non-linear data).

Linear Regression

Decision Trees

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised Learning Linear Regression Decision Trees Random Forests K-Nearest Neighbors K-Means Clustering Support Vector Machines Naive Bayes scikit-learn Basics Train/Test Split Cross-Validation

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