A Hands-On Introduction to Machine Learning
An accessible guide for beginners and professionals in machine learning
Format:Hardback
Publisher:Cambridge University Press
Published:29th Dec '22
Should be back in stock very soon
This textbook serves as a comprehensive guide to machine learning, ideal for beginners and professionals. A Hands-On Introduction to Machine Learning covers essential topics and offers practical exercises.
In A Hands-On Introduction to Machine Learning, readers are guided through the intricacies of machine learning with clarity and accessibility. The book is filled with real-world examples and industry insights, ensuring that learners can relate theoretical concepts to practical applications. It is tailored for those with only a basic understanding of technology, making it an excellent resource for students and professionals alike, particularly those new to the field of computer science.
The content covers a wide range of essential topics, including supervised and unsupervised learning, neural networks, reinforcement learning, and the ethical considerations that continue to challenge the industry. Python is the primary programming language used throughout the book, but many exercises also provide solutions in R, offering additional flexibility for readers. This dual-language approach enhances the learning experience and accommodates a broader audience.
To further support learners, the book includes a suite of online resources, such as example syllabi, a solutions manual, and lecture slides. Students will also find datasets and code available online, allowing them to practice the concepts discussed in A Hands-On Introduction to Machine Learning. This self-contained introduction is designed for those with no prior programming or machine learning experience, making it an approachable and comprehensive guide to the subject.
'Written by a great teacher who truly understands the material, the book is conversational and very approachable, while at the same time covering the material comprehensively. I really appreciated the organization, starting from fundamentals that the reader would know already, and then building knowledge structures from there.' Akhilesh Bajaj, The University of Tulsa
'A much-needed book for learning and teaching the essentials of machine learning for practical usage. It has comprehensive and up-to-date coverage on the practical aspects of machine learning. The chapters on cloud computing and responsible AI cover two topics particularly relevant to today's machine learning practices, yet rarely found at such depth and quality in other machine learning books. This book is self-contained and highly accessible to readers of diverse backgrounds. Materials are organized into five easy-to-follow parts while striking a delicate balance between breadth and depth, and between theory and practice. I highly recommend this book to those who need/want to equip themselves with practical hand-on machine learning skills to get their work done.' Haiping Lu, University of Sheffield
'… clearly and concisely introduces traditional and modern machine learning topics. The book is highly accessible for those who are very new to machine learning across diverse computing environments. Ethical issues that we need to pay more attention to are also discussed, and are a great feature.' Minwoo Lee, Department of Computer Science & School of Data Science, The University of North Carolina at Charlotte
'…an accessible textbook for students of machine learning. The presentations of algorithms are clear and supported by examples. The conceptual questions at the end of each chapter allow students to review key concepts, while hands-on problems prepare students to apply what they have learned to real situations. Shah's book is also a valuable tool for practitioners of machine learning.' Tony Diana, Lecturer, University of Maryland Baltimore County (UMBC)
'… an accessible yet far-reaching treatment of practical machine learning. Professor Shah leverages his years of experience creating, teaching, and applying machine learning, in academia as well as industry, to present material that ranges from classical topics to current trends. The pedagogy allows anyone - new or seasoned - to benefit by trying many hands-on problems in different application areas.' Rishabh Mehrotra, Director, Machine Learning at ShareChat
'… an approachable exposition of machine learning with theories and context based on real-life, practical applications. Professor Shah interweaves theoretical concepts, such as dimensionality reduction, gradient descent, and reinforcement learning, with hands-on examples that are easy to understand. This helps students in the classroom as well as other engineering practitioners who are approaching these topics for real-world use cases.' Madhu Kurup, Vice President, Indeed.com
'A Hands-On Introduction to Machine Learning by Chirag Shah is a very good data science textbook, starting from the basics, that covers many subjects not usually covered in introductory data science books, including cloud computing, deep learning, dimensionality reduction, bias and fairness for a responsible AI, and a comprehensive coverage of model evaluation (more about it below). The explanations are clear and many insights are present throughout the book … authoritative and clear, with well-thought-out examples and use cases and a coverage that rivals that of the best, more advanced books. I highly recommend this book. After reading it, you will understand why its author, Prof Chirag, has received many awards.' Paulo Cysne Rios Jr, Data Science Leader
'As a university instructor myself, I immediately appreciated author and University of Washington professor Chirag Shah's pedagogical approach. This is a gainful learning tool. Every chapter has excellent coverage of the typical machine learning (ML) topics coupled with very helpful 'Try It Yourself' sidebars that allow readers to exercise their understanding of the subjects as they progress through the material. Each chapter also includes a 'Conceptual Questions' and 'Hands-on Examples' feature at the end of each section … All in all I consider this book as a fine new entry into the field of machine learning and deep learning. I plan to add this title to the bibliography I give to my beginning data science students as an educational resource they can consume just after taking my intro class. Kudos to author Shah for seeing a need for this type of text!' Daniel D. Gutierrez, InsideBigData
ISBN: 9781009123303
Dimensions: 261mm x 209mm x 23mm
Weight: 1200g
500 pages