A Hands-On Introduction to Machine Learning

Chirag Shah (Autor)

Buch | Hardcover

500 Seiten

2022
Cambridge University Press (Verlag)
978-1-009-12330-3 (ISBN)

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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.

Dr. Chirag Shah is a Professor of Information Science at the University of Washington (UW) in Seattle, USA. Before UW, he was at Rutgers University. His research focuses on intelligent information access systems that are also fair, transparent, and trustworthy. Dr. Shah teaches in undergraduate, masters, and Ph.D. programs at UW, focusing on data science and machine learning. He has designed MOOCs and taught several tutorials and short courses at international venues. Dr. Shah has written several books, including the bestselling textbook A Hands-On Introduction to Data Science (2020). He has visited and worked with many tech companies, including Amazon, Brainly, Getty Images, Microsoft Research, and Spotify.

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.

Erscheinungsdatum	18.10.2021
Zusatzinfo	Worked examples or Exercises
Verlagsort	Cambridge
Sprache	englisch
Maße	209 x 261 mm
Gewicht	1200 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
ISBN-10	1-009-12330-0 / 1009123300
ISBN-13	978-1-009-12330-3 / 9781009123303
Zustand	Neuware