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Probabilistic Machine Learning

Kevin P. Murphy

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Eco‑Conscious Packaging

Binding

Hardback

Number of Pages

864

Age Group

All

Language

English

Book Summary

This 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.

Key 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
Learning outcomes: solid mathematical grounding, ability to model uncertainty, practical skills to implement and reproduce results, capacity to apply concepts to real-world problems
Illustration and writing style: clear, rigorous explanations paired with intuitive insights and concrete examples
Interactive and standout features: online Python code with libraries such as scikit-learn, JAX, PyTorch, and TensorFlow; browser-based notebooks for interactive exploration

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.

Product Details

Author

Kevin P. Murphy

Publisher

Penguin Random House

Number of Pages

864

Language

English

SKU

9780262046824

ISBN

9780262046824

Reading Age

All

Dimensions

21.3x3.3x23.5cm

Binding

Hardback

Probabilistic Machine Learning

₹ 11,000

Add to Bag

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