machine_learning_complete

Why we included this project

This repo packs 35 notebooks that take you from Python basics to deep learning in a logical order. You start with NumPy and Pandas for data manipulation, move into Matplotlib and Seaborn for visualization, then hit classical algorithms in Scikit-Learn and neural networks in TensorFlow/Keras. Each notebook leads with a plain-language overview of the algorithm, and the author leans on visuals to make abstract ideas click. Beyond the models, it covers the everyday data hygiene work: exploratory analysis, missing values, feature scaling, and encoding categorical columns. Everything runs in the browser through Colab, Deepnote, or nbviewer, so you can follow along without a local setup, and the maintainer welcomes corrections and pull requests.