This is an educational resource that teaches machine learning through interactive Jupyter Notebooks. Each algorithm is implemented from first principles using NumPy, with visualizations showing how weights evolve during training. It covers neural networks, logistic regression, perceptrons, PCA, K-means clustering, and gradient descent. The project is organized as a Jupyter Book with separate GitHub repositories containing the actual algorithm implementations.