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Curated programming, artificial intelligence, and software engineering books. Free to read and download for everyone.

scikit-learn user guide

scikit-learn developers

A comprehensive technical guide to scikit-learn, covering the tools and techniques needed to build machine learning models in Python. It includes supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, preprocessing, feature selection, model evaluation, cross-validation, hyperparameter tuning, pipelines, and dataset utilities. The guide also provides practical examples and API references for implementing machine learning workflows with scikit-learn

Scikit-learn Python

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Deep Learning with PyTorch

Eli Stevens ,Luca Antiga ,Thomas Viehmann

A practical guide to building and training deep learning models using PyTorch. The book introduces PyTorch tensors, neural networks, data loading, model training, loss functions, monitoring, and visualization through hands-on projects. It focuses on applying deep learning to real-world problems, including image classification and medical imaging, while teaching techniques such as data augmentation, model improvement, and fine-tuning.

Deep Learning Python Pytorch

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Introduction to Machine Learning with Python

Andreas C. Müller & Sarah Guido

A practical, beginner-friendly guide to understanding and applying Machine Learning using Python and scikit-learn. The book covers fundamental ML concepts, supervised and unsupervised learning, data preprocessing, feature engineering, model evaluation, parameter tuning, pipelines, and working with text data. It emphasizes hands-on implementation and real-world workflows rather than focusing heavily on mathematical theory, making it useful for learners beginning their journey in Machine Learning and Data Science.

ML(Machine Learning) Python

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