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

'… 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

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]