A Hands-On Introduction to Machine Learning

A self-contained and practical introduction that assumes no prior knowledge of programming or machine learning.

Chirag Shah (Author)

9781009123303, Cambridge University Press

Hardback, published 29 December 2022

500 pages
26.1 x 20.9 x 2.3 cm, 1.2 kg

'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

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.

Part I. Basic Concepts: 1. Teaching computers to write programs
2. Python
3. Cloud computing
Part II. Supervised Learning: 4. Regression
5. Classification-1
6. Classification-2
Part III. Unsupervised Learning: 7. Clustering
8. Dimensionality reduction
Part IV. Neural Networks: 9. Neural networks
10. Deep learning
Part V. Further explorations: 11. Reinforcement learning
12. Designing and evaluating ML systems
13. Responsible AI
Appendices.

Subject Areas: Machine learning [UYQM], Knowledge management [KJMV3]