Machine Learning with Python
Principles and Practical Techniques

A textbook covering the fundamentals of machine learning algorithms and their implementation using the Python programming language.

Parteek Bhatia (Author)

9781009170246, Cambridge University Press

Paperback / softback, published 26 March 2026

850 pages
15 x 15 x 3.6 cm, 1.351 kg

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.

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.

Subject Areas: Pattern recognition [UYQP]