ML From Scratch
Focus: numpy and education. Shares machine learning and from scratch with Homemade Machine Learning. Same 4.7 rating.
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Course
Homemade Machine Learning
Python examples of popular ML algorithms with interactive Jupyter demos.
Rating
4.7 / 5
Pricing
Free
Difficulty
Intermediate
Works on
Web
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What is Homemade Machine Learning?
Homemade Machine Learning is a collection of from-scratch Python implementations of popular machine learning algorithms, each with interactive Jupyter demos and the math explained step by step.
Third-party open-source projects. We are not affiliated with their authors; repositories are provided as-is by their maintainers. Review each project's license, code and security before installing. See our Disclaimer.
Pros: what Homemade Machine Learning does well
- Every algorithm implemented from scratch
- Interactive Jupyter demos
- Math explained alongside the code
Cons: where it falls short
- For learning, not production use
- Covers classical ML more than deep learning
Who Homemade Machine Learning is for
Learners who want to truly understand ML algorithms by building them.
Who can skip it
Practitioners who just need to apply scikit-learn.
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