Homemade-Machine-Learning

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Homemade Machine Learning provides Python examples of popular machine learning algorithms with interactive Jupyter demos. It explains the math behind each algorithm, allowing users to implement them from scratch for educational purposes.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is homemade-machine-learning?

Homemade Machine Learning is a GitHub repository offering Python implementations of widely used machine learning algorithms. Each algorithm is accompanied by detailed mathematical explanations and interactive Jupyter Notebook demos, enabling users to experiment with training data and configurations directly in their browser. The project emphasizes understanding the underlying mathematics by implementing algorithms from scratch, rather than relying on third-party libraries. It covers supervised learning (linear and logistic regression), unsupervised learning (K-means, anomaly detection), and neural networks (Multilayer Perceptron). This resource is ideal for students and developers looking to deepen their understanding of machine learning fundamentals.

Best used for

Ideal for students who need to learn machine learning algorithms from the ground up, understand the mathematical principles, and practice implementing them in Python. Especially valuable for those who prefer interactive demos and hands-on coding to solidify their knowledge.

Common actions

learn machine learning

understand algorithms

practice coding ML

explore data science

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Capabilities

Key features

Python algorithm examples
Interactive Jupyter demos
Mathematical explanations
Supervised learning examples
Unsupervised learning examples
Neural network examples

Target Audience

student

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of machine learning algorithms are covered?

The repository covers supervised learning algorithms like Linear and Logistic Regression, unsupervised learning algorithms such as K-means and Anomaly Detection, and neural networks including Multilayer Perceptrons. Each category includes multiple interactive demos to illustrate different applications.

Can I run the Jupyter Notebook demos without local installation?

Yes, you can view the demos directly in your browser using Jupyter NBViewer. If you wish to interact with the code and experiment, you can launch the notebooks in Binder by clicking the "Execute on Binder" link provided in NBViewer.

Is this project suitable for production use?

No, the project explicitly states that the implementations are "homemade" and not intended for production use. Its primary purpose is educational, focusing on understanding the mathematics and implementation details of machine learning algorithms from scratch.

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