🤖 Homemade Machine Learning
project
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
- AI
- ML
- Machine Learning
- Algorithms
- Python
About
Homemade Machine Learning implements popular machine learning algorithms from scratch in Python and explains the math behind each one. The goal isn't to call a ready-made library but to understand how the algorithms actually work.
It covers linear and logistic regression, k-means clustering, anomaly detection with a Gaussian distribution, and a multilayer perceptron neural network. Each algorithm has an interactive Jupyter notebook where you can change the training data and settings and immediately see the results, charts and predictions.
Most of the explanations follow Andrew Ng's machine learning course. The same algorithms are also available in MatLab/Octave as a separate project.
Achievements
12
