{"product_id":"mathematics-for-machine-learning-book-by-a-aldo-faisal-cheng-soon-ong-and-marc-peter-deisenroth","title":"Mathematics for Machine Learning\nBook by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth","description":"\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eBuild a solid foundation in the core math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, explained through practical Python examples\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eKey Features\u003c\/p\u003e\n\u003cp\u003eMaster linear algebra, calculus, and probability theory for ML\u003c\/p\u003e\n\u003cp\u003eBridge the gap between theory and real-world applications\u003c\/p\u003e\n\u003cp\u003eLearn Python implementations of core mathematical concepts\u003c\/p\u003e\n\u003cp\u003eBook Description\u003c\/p\u003e\n\u003cp\u003eMathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003ePhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eBy the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eWhat you will learn\u003c\/p\u003e\n\u003cp\u003eUnderstand core concepts of linear algebra, including matrices, eigenvalues, and decompositions\u003c\/p\u003e\n\u003cp\u003eGrasp fundamental principles of calculus, including differentiation and integration\u003c\/p\u003e\n\u003cp\u003eExplore advanced topics in multivariable calculus for optimization in high dimensions\u003c\/p\u003e\n\u003cp\u003eMaster essential probability concepts like distributions, Bayes' theorem, and entropy\u003c\/p\u003e\n\u003cp\u003eBring mathematical ideas to life through Python-based implementations\u003c\/p\u003e\n\u003cp\u003eWho this book is for\u003c\/p\u003e\n\u003cp\u003eThis book is for aspiring machine learning engineers, data scientists, software developers, and researchers who want to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of algebra and Python, and basic familiarity with machine learning tools are recommended.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eTable of Contents\u003c\/p\u003e\n\u003cp\u003eVectors and vector spaces\u003c\/p\u003e\n\u003cp\u003eThe geometric structure of vector spaces\u003c\/p\u003e\n\u003cp\u003eLinear algebra in practice spaces: measuring distances\u003c\/p\u003e\n\u003cp\u003eLinear transformations\u003c\/p\u003e\n\u003cp\u003eMatrices and equations\u003c\/p\u003e\n\u003cp\u003eEigenvalues and eigenvectors\u003c\/p\u003e\n\u003cp\u003eMatrix factorizations\u003c\/p\u003e\n\u003cp\u003eMatrices and graphs\u003c\/p\u003e\n\u003cp\u003eFunctions\u003c\/p\u003e\n\u003cp\u003eNumbers, sequences, and series\u003c\/p\u003e\n\u003cp\u003eTopology, limits, and continuity\u003c\/p\u003e\n\u003cp\u003eDifferentiation\u003c\/p\u003e\n\u003cp\u003eOptimization\u003c\/p\u003e\n\u003cp\u003eIntegration\u003c\/p\u003e\n\u003cp\u003eMultivariable functions\u003c\/p\u003e\n\u003cp\u003eDerivatives and gradients\u003c\/p\u003e\n\u003cp\u003eOptimization in multiple variables\u003c\/p\u003e\n\u003cp\u003eWhat is probability?\u003c\/p\u003e\n\u003cp\u003eRandom variables and distributions\u003c\/p\u003e\n\u003cp\u003eThe expected value\u003c\/p\u003e\n\u003cp\u003eThe maximum likelihood estimation\u003c\/p\u003e\n\u003cp\u003eIt's just logic\u003c\/p\u003e\n\u003cp\u003eThe structure of mathematics\u003c\/p\u003e\n\u003cp\u003eBasics of set theory\u003c\/p\u003e\n\u003cp\u003eComplex numbers\u003c\/p\u003e","brand":"Book Lovers BD","offers":[{"title":"Paperback","offer_id":55034758824227,"sku":null,"price":1000.0,"currency_code":"BDT","in_stock":true},{"title":"Hardcover","offer_id":55034758856995,"sku":null,"price":1130.0,"currency_code":"BDT","in_stock":true},{"title":"Ebook","offer_id":55034758889763,"sku":null,"price":150.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0954\/5872\/2083\/files\/IMG-5722.jpg?v=1779727746","url":"https:\/\/www.bookloversbd.shop\/products\/mathematics-for-machine-learning-book-by-a-aldo-faisal-cheng-soon-ong-and-marc-peter-deisenroth","provider":"Book Lovers BD","version":"1.0","type":"link"}