New Textbook for Undergraduate & Postgraduate Machine Learning / AI Courses

[Parteek Bhatia]

Dear Sir/Madam,

I hope this message finds you well.

I’m delighted to announce the release of my latest book, Machine Learning with Python: Principles and Practical Techniques, published by Cambridge University Press, UK. This book is the culmination of my two decades of teaching, research, and applied work in machine learning.

 Designed as a comprehensive textbook and lab companion, the book bridges the gap between foundational theory and practical implementation. Each conceptual chapter is immediately followed by a hands-on implementation chapter, enabling students to reinforce their learning through Python-based coding exercises using real-world datasets.

Book Highlights

The book offers a structured, accessible journey through essential and advanced machine learning topics:

• Getting Started with Machine Learning
• Python Essentials
• Data Preprocessing & Practice
• Simple, Multiple & Polynomial Regression
• Classification (KNN, Logistic, SVM, etc.)
• Clustering (K-Means, Hierarchical)
• Association Rule Mining
• Artificial Neural Networks
• Deep Learning & Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Genetic Algorithms for ML

Instructor Resources Included

To support seamless integration into your curriculum, the following materials are available:

PowerPoint Slides:
Available to Instructors Upon Request (write to parteek...@gmail.com)

GitHub Repository with Colab-Ready Notebooks:
bhatiaparteek/ml_with_python

Online Version:

Machine Learning with Python | Cambridge Aspire website

Request an Examination Copy (For Faculty):
Machine Learning with Python - Request Examination Copy

Buy on Amazon India:
https://www.amazon.in/Machine-Learning-Python-Principles-Techniques/dp/1009170244/

Why Adopt This Textbook for Your ML Course?

• Curriculum-Aligned: Designed to meet the learning objectives of both undergraduate and postgraduate ML/AI programs.
• Comprehensive Coverage: Spans foundational topics (e.g., regression, classification) to advanced concepts (CNNs, RNNs, genetic algorithms).
• Integrated Hands-On Learning: Every theory chapter is followed by a dedicated implementation chapter, serving as a lab manual for practical reinforcement.
• Industry-Relevant Practice: Focuses on real-world Python implementation using Jupyter Notebooks and Google Colab.
• Instructor-Friendly Resources: Comes with editable lecture slides, datasets, and ready-to-run code to simplify course delivery.
• Versatile Adoption: Perfect for courses in computer science, data science, artificial intelligence, and related domains.

If this aligns with your course objectives, I would be honored if you consider adopting it as a primary textbook or recommending it for departmental or library acquisition.

Please feel free to reach out for sample chapters, tailored lecture slides, or to explore guest lectures, curriculum collaborations, or webinars.

 About the Author

Dr. Parteek Bhatia, Associate Professor, School of Electrical Engineering and Computer Science, Washington State University, Pullman, USA, Former Professor, Thapar Institute of Engineering and Technology, Patiala, India. With 25+ years of experience, Dr. Bhatia is the author of several best-selling textbooks in AI and data science. His student-first, application-driven approach has made him a sought-after speaker and trainer across the globe.

Website: www.parteekbhatia.com

Warm Regards

Parteek Kumar, Ph.D.
Associate Professor 
School of Electrical Engineering and Computer Science
Washington State University, Pullman

Gold Tier Campus Ambassador
NVIDIA Deep Learning Institute

🌐 www.parteekbhatia.com
📘 Machine Learning with Python — Cambridge Aspire