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Tivadar Danka
Mathematics of Machine Learning
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7375506Mathematics of Machine Learninghttps://www.gandhi.com.mx/mathematics-of-machine-learning-9781837027866/phttps://gandhi.vtexassets.com/arquivos/ids/6965352/image.jpg?v=638881159277130000886985MXNPackt PublishingInStock/Ebooks/7010089Mathematics of Machine Learning886985https://www.gandhi.com.mx/mathematics-of-machine-learning-9781837027866/phttps://gandhi.vtexassets.com/arquivos/ids/6965352/image.jpg?v=638881159277130000InStockMXN99999DIEbook20259781837027866_W3siaWQiOiIzMzg4YWEyMC1mZmIwLTQwNDItYTZlNS1lNGZjODVmYzdkY2EiLCJsaXN0UHJpY2UiOjk4NSwiZGlzY291bnQiOjk5LCJzZWxsaW5nUHJpY2UiOjg4NiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDUtMzBUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781837027866_<p><b>Master the math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, complete with Python examples</b></p><h2>Key Features</h2><ul><li>Master linear algebra, calculus, and probability theory for ML</li><li>Understand mathematical structures behind machine learning algorithms</li><li>Learn Python implementations of core mathematical concepts</li><li>Develop skills to optimize, customize, and analyze machine learning solutions</li><li>Bridge the gap between theory and real-world applications</li></ul><h2>Book Description</h2>Mathematics 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, youll explore the core disciplines of linear algebra, calculus, and probability theory, essential for mastering advanced machine learning concepts. The book balances theory and application, offering clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, youll not only learn the mathematics but also how to implement and use these ideas in real-world scenarios, such as optimizing algorithms or solving specific challenges in neural network training. Whether you aim to deepen your theoretical knowledge or enhance your capacity to solve complex machine learning problems, this book provides the structured guidance you need. By the end of this book, youll gain the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.<h2>What you will learn</h2><ul><li>Core concepts of linear algebra, including matrices, eigenvalues, and decompositions</li><li>Fundamental principles of calculus, including differentiation and integration</li><li>Advanced topics in multivariable calculus for optimization in high dimensions</li><li>Essential probability concepts like distributions, Bayes theorem, and entropy</li><li>Python-based implementations to bring mathematical ideas to life</li><li>The superpower of mathematical and scientific thinking, with applications to data science and machine learning</li></ul><h2>Who this book is for</h2><p>This book is for aspiring and practicing machine learning engineers, data scientists, and software developers who wish to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of Python and a basic familiarity with machine learning tools are recommended.</p>...(*_*)9781837027866_<p><b>Build 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 Purchase of the print or Kindle book includes a free PDF eBook </b></p><h2>Key Features</h2><ul><li>Master linear algebra, calculus, and probability theory for ML</li><li>Bridge the gap between theory and real-world applications</li><li>Learn Python implementations of core mathematical concepts</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h2>Book Description</h2>Mathematics 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, youll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts. PhD mathematician turned ML engineer Tivadar Dankaknown for his intuitive teaching style that has attracted 100k followersguides 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, youll 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. By the end of this book, youll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements. <h2>What you will learn</h2><ul><li>Understand core concepts of linear algebra, including matrices, eigenvalues, and decompositions</li><li>Grasp fundamental principles of calculus, including differentiation and integration</li><li>Explore advanced topics in multivariable calculus for optimization in high dimensions</li><li>Master essential probability concepts like distributions, Bayes theorem, and entropy</li><li>Bring mathematical ideas to life through Python-based implementations</li></ul><h2>Who this book is for</h2><p>This 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. </p>...(*_*)9781837027866_<p><b>Build 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 Purchase of the print or Kindle book includes a free PDF eBook </b></p><h2>Key Features</h2><ul><li>Master linear algebra, calculus, and probability theory for ML</li><li>Bridge the gap between theory and real-world applications</li><li>Learn Python implementations of core mathematical concepts</li></ul><h2>Book Description</h2>Mathematics 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, youll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts. PhD mathematician turned ML engineer Tivadar Dankaknown for his intuitive teaching style that has attracted 100k followersguides 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, youll 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. By the end of this book, youll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements. <h2>What you will learn</h2><ul><li>Understand core concepts of linear algebra, including matrices, eigenvalues, and decompositions</li><li>Grasp fundamental principles of calculus, including differentiation and integration</li><li>Explore advanced topics in multivariable calculus for optimization in high dimensions</li><li>Master essential probability concepts like distributions, Bayes theorem, and entropy</li><li>Bring mathematical ideas to life through Python-based implementations</li></ul><h2>Who this book is for</h2><p>This 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. </p>...9781837027866_Packt Publishinglibro_electonico_9781837027866_9781837027866Tivadar DankaInglésMéxico2025-05-30T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/packt-epub-17bbbca7-fe34-48a0-bbbe-55ad9fa56884.epub2025-05-30T00:00:00+00:00Packt Publishing