Machine Learning with Python

Principles and Practical Techniques

Parteek Bhatia (Thapar University, India)

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English

Cambridge University Press
31 May 2025

Pattern recognition; Computer vision

Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner.

By: Parteek Bhatia (Thapar University India)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
ISBN: 9781009170246
ISBN 10: 1009170244
Pages: 850
Publication Date: 31 May 2025
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Forthcoming

Acknowledgements; Preface; Chapter 1. Beginning with Machine Learning; Chapter 2. Introduction to Python; Chapter 3. Data Pre-processing; Chapter 4. Implementing Data Pre-processing in Python; Chapter 5. Simple Linear Regression; Chapter 6. Implementing Simple Linear Regression; Chapter 7. Multiple Linear Regression and Polynomial Linear Regression; Chapter 8. Implementing Multiple Linear Regression and Polynomial Linear Regression; Chapter 9. Classification; Chapter 10. Support Vector Machine Classifier; Chapter 11. Implementing Classification; Chapter 12. Clustering; Chapter 13. Implementing Clustering; Chapter 14. Association Mining; Chapter 15. Implementing Association Mining; Chapter 16. Artificial Neural Network; Chapter 17. Implementing the Artificial Neural Network; Chapter 18. Deep Learning and Convolutional Neural Network; Chapter 19. Implementing Convolutional Neural Network; Chapter 20. Recurrent Neural Network; Chapter 21. Implementing Recurrent Neural Network; Chapter 22. Genetic Algorithm for Machine Learning; Index.