New Textbook for Undergraduate & Postgraduate Machine Learning / AI Courses

Machine Learning with Python: Principles and Practical Techniques

Designed for undergraduate and postgraduate ML / AI courses

I am pleased to share the release of my new textbook, Machine Learning with Python: Principles and Practical Techniques, published by Cambridge University Press (UK).

This book has been carefully designed for undergraduate and postgraduate courses in Machine Learning, Artificial Intelligence, and Data Science, and can be used both as a primary course textbook and as a structured lab companion.

What Makes This Book Different

Unlike traditional machine learning textbooks that separate theory and practice, this book follows a paired learning model:

• Each conceptual chapter is immediately followed by a dedicated Python implementation chapter
• Students reinforce learning through real-world datasets, Jupyter notebooks, and Colab-ready labs
• The structure naturally supports lecture + lab delivery within the same course timeline

This approach helps students move seamlessly from understanding concepts to applying them in practice.

Topics Covered

• Machine Learning Foundations & Python Essentials
• Data Preprocessing & Practical Workflows
• Regression (Simple, Multiple, Polynomial)
• Classification (KNN, Logistic Regression, SVM, etc.)
• Clustering (K-Means, Hierarchical)
• Association Rule Mining
• Artificial Neural Networks
• Deep Learning & Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Genetic Algorithms for Machine Learning

The progression is suitable for introductory, intermediate, and advanced ML/AI courses.

Instructor Resources

To support smooth course adoption, the following instructor materials are available:

• PowerPoint lecture slides
• GitHub repository with Colab-ready notebooks and datasets
• Structured lab content aligned chapter-by-chapter

Instructor resources and slides are available upon request:

📧 parteek.bhatia@gmail.com

Online Access

• Cambridge Aspire (Digital Access):
• Overview of Machine Learning | Machine Learning with Python | Cambridge Aspire website
• Amazon India (Print Edition):
• https://www.amazon.in/Machine-Learning-Python-Principles-Techniques/dp/1009170244

Why Consider This Text for Your Course?

• Curriculum-aligned for UG & PG ML / AI programs
• Balanced depth: foundations to modern deep learning
• Integrated lab-first design, ideal for flipped or hybrid classrooms
• Industry-relevant Python workflows using Jupyter & Colab
• Instructor-friendly, reducing preparation time while increasing student engagement

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

I am also happy to share sample chapters, customized lecture slides, or explore guest lectures, workshops, or curriculum collaborations.

About the Author

Dr. Parteek Bhatia

Associate Professor

School of Electrical Engineering & Computer Science

Washington State University, Pullman, USA

With over 25 years of teaching and research experience, Dr. Bhatia is the author of several best-selling textbooks in Machine Learning, Data Science, and AI. His work emphasizes student-first pedagogy and application-driven learning across global academic and industry audiences.

Website: https://www.parteekbhatia.com

Warm Regards,

Dr. Parteek Bhatia