Article June 21, 2023

Mathematics for Machine Learning

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. From the Publisher <img alt="Mathematics for Machine Learning, Cambridge University Press, linear algebra" src="https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/grey-pixel.gif" class="a-spacing-base a-lazy-loaded" data-src="https://m.media-amazon.com/images/S/aplus-media/vc/dca1fdaa-a479-4d71-838d-82dfd27029ba.__CR0,0,970,600_PT0_SX970_V1___.jpg"><img alt="Mathematics for Machine Learning, Cambridge University Press, linear algebra" src="https://m.media-amazon.com/images/S/aplus-media/vc/dca1fdaa-a479-4d71-838d-82dfd27029ba.__CR0,0,970,600_PT0_SX970_V1___.jpg"> Publisher ‏ : ‎ Cambridge University Press; 1st edition (April 23, 2020) Language ‏ : ‎ English Paperback ‏ : ‎ 398 pages ISBN-10 ‏ : ‎ 110845514X ISBN-13 ‏ : ‎ 978-1108455145 Item Weight ‏ : ‎ 1.76 pounds Dimensions ‏ : ‎ 7 x 0.92 x 10 inches

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[ad_1] 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.
From the Publisher

Mathematics for Machine Learning, Cambridge University Press, linear algebra

Publisher ‏ : ‎ Cambridge University Press; 1st edition (April 23, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 398 pages
ISBN-10 ‏ : ‎ 110845514X
ISBN-13 ‏ : ‎ 978-1108455145
Item Weight ‏ : ‎ 1.76 pounds
Dimensions ‏ : ‎ 7 x 0.92 x 10 inches

[ad_2]

Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners

*Start your Data Science career using Python today!* Are you ready to start your new exciting career? Ready to crush your machine learning career goals? Are you overwhelmed with complexity of the books on this subject? Then let this breezy and fun little book on Python and machine learning models make you a data scientist in 7 days! First part of this book introduces Python basics including: •Data Structures like Pandas •Foundational libraries like Numpy, Seaborn and Scikit-Learn Second part of this book shows you how to build predictive machine learning models step by step using techniques such as: •Regression analysis •Decision tree analysis •Training and testing data models •Tensor Flow, Keras and PyTorch •Additional data science concepts like Classification Analysis, Clustering, Association Learning and Dimension Reduction The final part of the book provides a structured framework on how to solve real world problems using data science and how to structure your data science project. After reading this book you will be able to: •Code in Python with confidence •Build new machine learning models from scratch •Know how to clean and prepare your data for analytics •Speak confidently about statistical analysis techniques Data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world! If you are on the fence about making the leap to a new and lucrative career, this is the book for you! What sets this book apart from other books on the topic of Python and Machine learning: •Step by step code examples and explanation •Complex concepts explained visually •Real world applicability of the machine learning models introduced •Bonus free code samples that you can try yourself without any prior experience in Python! What do I need to get started? You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and machine learning and start lucrative and rewarding career! Ready to dive in to the exciting world of Python and Machine Learning? Then scroll up to the top and hit that BUY BUTTON! ASIN ‏ : ‎ B084WGCMG1 Publication date ‏ : ‎ February 16, 2020 Language ‏ : ‎ English File size ‏ : ‎ 2363 KB Text-to-Speech ‏ : ‎ Enabled Screen Reader ‏ : ‎ Supported Enhanced typesetting ‏ : ‎ Enabled X-Ray ‏ : ‎ Not Enabled Word Wise ‏ : ‎ Not Enabled Sticky notes ‏ : ‎ On Kindle Scribe Print length ‏ : ‎ 70 pages

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Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems

Cyber-solutions to real-world business problemsArtificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:Understand how specific business problems are addressed by innovative machine learning methodsExplore how current artificial intelligence applications improve performance and increase efficiency in various situationsExpand your knowledge of recent AI advancements in technologyGain insight on the future of AI and its increasing role in business and industryArtificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce. Publisher ‏ : ‎ Wiley; 1st edition (May 28, 2019) Language ‏ : ‎ English Hardcover ‏ : ‎ 352 pages ISBN-10 ‏ : ‎ 1119548217 ISBN-13 ‏ : ‎ 978-1119548218 Item Weight ‏ : ‎ 1.2 pounds Dimensions ‏ : ‎ 5.7 x 1 x 8.6 inches

ai-book Jun 23, 2023

Pattern Recognition and Machine Learning (Information Science and Statistics)

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. Publisher ‏ : ‎ Springer (August 17, 2006) Language ‏ : ‎ English Hardcover ‏ : ‎ 738 pages ISBN-10 ‏ : ‎ 0387310738 ISBN-13 ‏ : ‎ 978-0387310732 Item Weight ‏ : ‎ 4.73 pounds Dimensions ‏ : ‎ 7.7 x 1.3 x 10.2 inches

ai-book Jun 23, 2023

Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. Publisher ‏ : ‎ Bradford Books; second edition (November 13, 2018) Language ‏ : ‎ English Hardcover ‏ : ‎ 552 pages ISBN-10 ‏ : ‎ 0262039249 ISBN-13 ‏ : ‎ 978-0262039246 Item Weight ‏ : ‎ 2.6 pounds Dimensions ‏ : ‎ 7.25 x 1.48 x 9.31 inches

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Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners

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Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)