Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

• Learn applied machine learning with a solid foundation in theory
• Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

• Explore frameworks, models, and techniques for machines to learn from data
• Use scikit-learn for machine learning and PyTorch for deep learning
• Train machine learning classifiers on images, text, and more
• Build and train neural networks, transformers, and boosting algorithms
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.