Get Free Shipping on orders over $79
Machine Learning with Python : Principles and Practical Techniques - Parteek Bhatia

Machine Learning with Python

Principles and Practical Techniques

By: Parteek Bhatia

Paperback | 26 March 2026

At a Glance

Paperback


RRP $79.95

$78.75

or 4 interest-free payments of $19.69 with

 or 

Ships in 5 to 7 business days

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.

More in Pattern Recognition

Reinforcement Learning Foundations - Aviv  Tamar
Human-Centered Machine Learning - Rebecca Fiebrink
Mathematics for Machine Learning - Marc Peter Deisenroth

RRP $79.95

$61.75

23%
OFF
Exploring GeoAI : Tools and Workflows - Ismael Chivite
Exploring GeoAI : Tools and Workflows - Ismael Chivite
Complexity in Computer Science - Thomas  Watson
AI and Computational Intelligence for Environmental Sustainability - Anand, Ph.D.  and Director at  IoT and Intelligent Systems Lab, Duy Tan University, Vietnam) Nayyar
Llms and Generative AI for Healthcare : The Next Frontier - Kerrie Holley
Reinforcement Learning for Finance : A Python-Based Introduction - Yves Hilpisch