SahilAI v0.2.0

Learn machine learning by using it and understanding the mathematics behind it.

SahilAI is an early educational machine-learning framework by Sahil Khan. It is designed so students can import algorithms in Python, train them, and explore the calculations step by step.

Installation from source

Requires Python 3.10 or newer.

python -m pip install -e .

Import your own algorithms

from sahilai.linear_model import EasyLearn, ComplexLearning

X = [[1], [2], [3], [4], [5]]
y = [20, 30, 40, 50, 60]

# EasyLearn: closed-form least-squares solution, explained simply
easy = EasyLearn()
easy.fit(X, y)
print(easy.predict([[6]]))

# ComplexLearning: gradient-descent version, with training history
complex_model = ComplexLearning(learning_rate=0.01, epochs=5000)
complex_model.fit(X, y, verbose=True)
print(complex_model.predict([[6]]))
print(complex_model.loss_history_[-1])

These two classes are educational implementations, not production replacements for scikit-learn. This first version supports one numerical feature.

Terminal learning commands

sahilai start
sahilai lesson 1
sahilai lesson 2
sahilai lesson 3
sahilai demo linear

Mathematical details

Both models use the linear equation:

prediction = weight * x + bias

EasyLearn computes the least-squares solution directly using the closed-form equations for a one-feature linear regression.

ComplexLearning optimizes mean squared error with gradient descent:

MSE = (1/n) * sum((prediction - target)^2)
dMSE/dw = (2/n) * sum((prediction - target) * x)
dMSE/db = (2/n) * sum(prediction - target)

Parameter update:

parameter = parameter - learning_rate * gradient

Tests

python -m unittest discover -s tests -v

Roadmap

1. More lessons and interactive exercises
2. Multiple input features
3. Classification algorithms
4. Tensors and neural-network layers
5. Backpropagation and deep learning
6. Documentation and a public PyPI release

License

MIT.