Minimal ML Algorithm Implementations for Learning Internals

Ever found yourself staring at a scikit-learn model, wondering what actually happens inside when you call .fit()? You read the documentation, see equations, but the real implementation is buried in optimized C or complex abstractions. This repo is for you.

MLAlgorithms is a collection of minimal, readable implementations of classic machine learning algorithms. No black boxes, no production-ready optimizations — just clean Python code that shows you the math in action.

What It Does

The repo contains standalone implementations of algorithms like:

• Linear Regression (Ordinary Least Squares, Ridge, Lasso)
• Logistic Regression
• K-Nearest Neighbors
• K-Means Clustering
• Decision Trees (ID3, CART)
• Naive Bayes (Gaussian, Multinomial)
• Principal Component Analysis (PCA)
• Perceptron and Neural Networks (basic)
• Support Vector Machines (SMO-based)
• And a few more

Each algorithm is written in pure Python, usually under 200 lines, with numpy as the only dependency. The focus is on readability — variable names match textbook notation, comments explain steps, and the code follows the algorithm pseudocode closely.

Why It's Cool

• Learning first, performance second. You won't find early stopping, convergence tolerances, or sparse matrix optimizations. You will find the core logic, uncluttered.
• Compare implementations side-by-side. Want to see how Ridge Regression differs from Lasso in code? Both are in the same module. Spot the difference instantly.
• Test-driven understanding. Each algorithm has a test.py that runs it on a tiny dataset (e.g., iris) and compares results to scikit-learn. You can literally watch the values converge.
• No magic. The fit(), predict(), and transform() methods mirror sklearn, but you can step through them in a debugger. No hidden abstractions.
• Great for teaching. If you're explaining ML to others, this is a goldmine. You can point to the code and say "that's the cost function being minimized" — and they'll see it.

How to Try It

Clone the repo, install numpy, and run any test:

git clone https://github.com/rushter/MLAlgorithms.git
cd MLAlgorithms
pip install numpy
python mla/test/linear_regression.py  # outputs comparison with sklearn

You can also import algorithms directly:

from mla.linear_regression import LinearRegression
model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X_test)

No setup.py, no heavy dependencies. Just copy the file you need into your project, or hack on it to see what happens when you change the learning rate.

Final Thoughts

This is not a library you'd use in production — use scikit-learn or XGBoost for that. But if you've ever felt like ML is just "call a method and get results," this repo bridges the gap. It's the difference between knowing that gradient descent works and seeing it work, loop by loop, update by update.

Whether you're studying for interviews, teaching a workshop, or just satisfying your own curiosity, MLAlgorithms gives you the raw, unfiltered version of the math. Run it, break it, fix it — that's how you learn.

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