Comprehensive ML Implementations in Pure NumPy
techNmak · x · 2026-07-11
The author recommends the open-source project ML-From-Scratch as a great resource for learning machine learning.
The project implements algorithms from scratch using pure NumPy, focusing on readability and mathematical understanding rather than engineering performance. It covers:
- Supervised learning: Linear/Logistic regression, Decision Trees, Random Forests, Gradient Boosting, XGBoost, SVM, Naive Bayes
- Unsupervised learning: k-means, DBSCAN, PCA, Gaussian Mixture Models
- A mini deep learning framework: Convolution, Pooling, Batch Normalization, Dropout, RNN
- A working GAN and a DQN trained on CartPole-v1
The author suggests that if you already know these algorithms and want to solidify your understanding through end-to-end implementations, this repo is worth your time. It currently has around 32,000 stars, uses the MIT license, and is written entirely in Python.
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