susanli2016/Machine-Learning-with-Python resource

Health v2 · maintenance only

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4592 stars · 4759 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter Notebooks demonstrating common machine learning algorithms in Python, covering regression, classification, clustering, and dimensionality reduction techniques. It serves as a hands-on educational resource with example code for algorithms like SVM, random forests, XGBoost, and PCA.

Use cases

• learn machine learning algorithms with python code examples
• understand how decision trees and random forests work
• see examples of logistic regression and svm in jupyter notebooks
• study clustering algorithms like kmeans and hierarchical clustering
• get started with xgboost and pca implementations
• find reference code for common scikit-learn models

When to choose

• you are learning machine learning and want runnable notebook examples
• you need reference implementations of classic ml algorithms
• you prefer studying code over theory-heavy textbooks

When to avoid

• you need production-ready or maintained ml code
• you want a library or framework rather than example notebooks
• you need a license for reuse in your own projects

Facets

learning-resource · maturity maintenance

machine-learning data-science machine-learning data-science tutorials python jupyter-notebooks scikit-learn algorithms educational classification clustering dimensionality-reduction

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