Machine Learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python / Sebastian Raschka, Yuxi (Hayden) Liu and Vahid Mirjalili

Includes index.

Giving Computers the Ability to Learn from Data -- Training Simple Machine Learning Algorithms for Classification -- A Tour of Machine Learning Classifiers Using Scikit-Learn -- Building Good Training Datasets - Data Preprocessing -- Compressing Data via Dimensionality Reduction -- Learning Best Practices for Model Evaluation and Hyperparameter Tuning -- Combining Different Models for Ensemble Learning -- Applying Machine Learning to Sentiment Analysis -- Predicting Continuous Target Variables with Regression Analysis -- Working with Unlabeled Data - Clustering Analysis -- Implementing a Multilayer Artificial Neural Network from Scratch.

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.