Machine Learning with PyTorch and Scikit-Learn

Few machine learning books manage to cover both classical techniques and modern deep learning with equal depth, but Machine Learning with PyTorch and Scikit-Learn does exactly that across its 770 pages. The author team brings genuinely strong credentials — Sebastian Raschka is well known for his clear technical writing and research background, joined by Yuxi (Hayden) Liu and Vahid Mirjalili, both experienced in applied machine learning. Together they walk through the full landscape: traditional ML techniques using Scikit-Learn, then a thorough transition into deep learning fundamentals and practical model-building using PyTorch. The book balances theoretical grounding with extensive hands-on coding examples, making it suitable both for readers building foundational understanding and for those who already know the basics and want a deeper, more rigorous treatment. Its substantial length reflects genuine depth rather than padding, covering enough ground to function as a long-term reference rather than a one-time read. For practitioners who want a single, comprehensive resource spanning classical and modern machine learning approaches, this remains one of the field’s most trusted options.

Authors: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili.