This repository provides a tutorial implementation of AutoML for batch and online learning.
Open source
24 alternativesHomemade Machine Learning
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
About Homemade Machine Learning
Homemade Machine Learning is a repository of Python examples of machine learning algorithms with the mathematics behind them explained. Each algorithm includes an interactive Jupyter Notebook demo for adjusting training data and configuration and viewing results, charts and predictions in the browser.
The implementations are written from scratch for learning rather than using third-party library one-liners, and are not intended for production use. Topics include supervised learning such as linear and logistic regression, and unsupervised learning such as K-means clustering.
Key features
- Python implementations of algorithms built from scratch
- Interactive Jupyter Notebook demos with training data controls
- Math explanations with links for further reading
- Linear regression demos including univariate, multivariate, non-linear examples
- Logistic regression demos including Iris, microchip, MNIST examples
- K-means clustering example with theory explanation
Best for: Learners who want to study machine learning mathematics through Python code and interactive demos.
Good for
- Interactive demos show results, charts and predictions in the browser
- Math explanations accompany code implementations
- Open source repository with Python examples
Watch out for
- Not intended for production use
Homemade Machine Learning at a glance
- Category
- Education & Learning › AI Learning & Guides
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Homemade Machine Learning FAQ
Is the code meant for production use?
No. The repository states the homemade implementations are for practicing from scratch and are not intended for production.
What course are the explanations based on?
In most cases, the explanations are based on a machine learning course by Andrew Ng.
Is there an Octave or MATLAB version?
Yes. The repository points to the machine-learning-octave project for an Octave/MATLAB version.
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