Description
Book Synopsis: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Details
Are you struggling to understand the complex mathematical tools behind machine learning? Look no further! Our Mathematics for Machine Learning Book is the ultimate guide you've been waiting for. Designed specifically for data science or computer science students, this self-contained textbook bridges the gap between mathematical and machine learning concepts. No more wasting time on disparate courses – we've got everything you need in one place.
With a minimum of prerequisites, our book introduces all the fundamental mathematical tools you need to tackle machine learning. From linear algebra and matrix decompositions to optimization and probability, we cover it all. Our step-by-step approach ensures that even beginners can grasp these concepts with ease. Say goodbye to confusion and hello to a solid foundation!
But that's not all – our book doesn't just stop at theory. We take these mathematical concepts and apply them to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. With our derivations as your starting point, you'll be well-equipped to dive into deeper machine learning texts.
We understand that practical experience is crucial for understanding mathematical concepts. That's why every chapter of our book is packed with worked examples and exercises to test your understanding. We go beyond theory and provide you with real-world scenarios to apply your newfound knowledge.
Worried about putting theory into practice? Don't be! Our book also offers programming tutorials on our web site. With hands-on experience, you'll be able to see the tangible results of applying mathematical concepts to machine learning. Start building your skills and boosting your confidence now.
Don't let complex mathematics hold you back from mastering machine learning. Get your copy of Mathematics for Machine Learning Book today and unlock the potential of this exciting field. Empower yourself with knowledge and take the first step towards becoming a machine learning expert.
Order now and start your mathematical journey to machine learning success!
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