patrickloeber/MLfromscratch
Educational repository implementing 12 classic machine learning algorithms from scratch using only numpy.
Not currently ranked — collecting fresh signals.
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This repository provides implementations of fundamental machine learning algorithms including KNN, Linear/Logistic Regression, SVM, Decision Trees, Random Forests, Naive Bayes, Perceptron, PCA, K-Means, AdaBoost, and LDA. All algorithms are implemented from scratch in pure numpy to demonstrate the underlying mathematics, rather than relying on existing ML libraries. The project serves as a learning resource with accompanying YouTube tutorials explaining both the math and code.
Frequently asked
- What is patrickloeber/MLfromscratch?
- Educational repository implementing 12 classic machine learning algorithms from scratch using only numpy.
- Is MLfromscratch open source?
- Yes — patrickloeber/MLfromscratch is open source, released under the MIT license.
- What language is MLfromscratch written in?
- patrickloeber/MLfromscratch is primarily written in Python.
- How popular is MLfromscratch?
- patrickloeber/MLfromscratch has 1.6k stars on GitHub.
- Where can I find MLfromscratch?
- patrickloeber/MLfromscratch is on GitHub at https://github.com/patrickloeber/MLfromscratch.