{"product_id":"9780262046824","title":"Probabilistic Machine Learning","description":"\u003cdiv\u003eThis non-fiction guide to machine learning uses probabilistic modeling and Bayesian decision theory as its unifying lens. It offers a rigorous, up-to-date introduction that blends math, intuition, and hands-on practice for readers with an interest in data-driven thinking. The tone is clear, thoughtful, and encouraging, helping learners build confidence as they navigate complex concepts. The content is presented as a structured, concept-first treatment that moves from mathematical foundations to practical applications. Readers encounter detailed explanations, worked examples, and real-world perspectives that make abstract ideas feel tangible. A strong emphasis on reproducible practice, with online Python code and browser-based notebooks, sets this text apart and makes learning active and engaging. In addition to core theory, the book covers essential techniques and modern developments in a coherent progression. Topics include linear and logistic regression, deep neural networks, transfer learning, and unsupervised learning, all framed within probabilistic thinking. End-of-chapter exercises reinforce understanding, while an appendix of notation provides quick reference and clarity throughout the journey. \u003cul\u003e\n\u003cli\u003eKey content elements: probabilistic modeling, Bayesian decision theory, linear and logistic regression, deep learning foundations, transfer learning, unsupervised learning, mathematical background in linear algebra and optimization, end-of-chapter exercises, notation appendix\u003c\/li\u003e\n\u003cli\u003eLearning outcomes: solid mathematical grounding, ability to model uncertainty, practical skills to implement and reproduce results, capacity to apply concepts to real-world problems\u003c\/li\u003e\n\u003cli\u003eIllustration and writing style: clear, rigorous explanations paired with intuitive insights and concrete examples\u003c\/li\u003e\n\u003cli\u003eInteractive and standout features: online Python code with libraries such as scikit-learn, JAX, PyTorch, and TensorFlow; browser-based notebooks for interactive exploration\u003c\/li\u003e\n\u003c\/ul\u003e Readers finish with a robust foundation in probabilistic machine learning, enhanced problem-solving instincts, and the curiosity to explore advanced topics. It builds confidence in applying statistical thinking to data, fosters an analytical mindset for algorithm design, and leaves a lasting impression of practical, theory-grounded learning.\u003c\/div\u003e","brand":"Crossword.in","offers":[{"title":"Default Title","offer_id":48540550529241,"sku":"9780262046824","price":10450.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0648\/3066\/9017\/files\/71GaJ8soqhL._SL1500.jpg?v=1779266944","url":"https:\/\/www.crossword.in\/products\/9780262046824","provider":"Crossword.in ","version":"1.0","type":"link"}