E-Books →Machine Learning for Engineers

Key Features:
1. NEP 2020 and AICTE Compliant: The structure and content are fully aligned with the multidisciplinary, practical, and skill-oriented framework of the National Education Policy (NEP) 2020 and the Outcome-Based Education (OBE) model of AICTE.
2. Engineering-Centric Approach: Unlike generic ML books, every chapter is infused with examples and case studies from Civil, Mechanical, Electrical, Chemical, and other engineering branches, making the content immediately relatable and applicable.
3. Progressive Learning Path: The book is logically structured into 10 chapters, starting from the absolute basics and progressively building up to more advanced topics like deep learning, NLP, and a full-fledged capstone project.
4. Hands-On Practical Examples: Learning is reinforced through easy-to-follow Python code snippets using popular libraries like Scikit-learn, Pandas, and TensorFlow/Keras. This practical approach ensures students can immediately apply what they learn.
5. Focus on Intuition and Visualization: Complex algorithms are explained using simple analogies, diagrams, and visualizations to build a strong conceptual foundation before delving into the mathematics.
6. Dedicated Chapter on Ethics: In line with modern curriculum demands, a full chapter is devoted to Responsible AI, covering critical topics like bias, fairness, accountability, and data privacy, preparing students for the ethical challenges of the profession.
7. Real-Life Capstone Project: The book culminates in a comprehensive capstone project on predictive maintenance. This chapter guides students step-by-step through a complete ML workflow, from data cleaning and feature engineering to model building and interpretation, solidifying their end-to-end understanding.
This book is also designed with the global engineering student in mind. The core concepts, ethical considerations, and practical applications discussed are universally relevant and align with the curricula of leading international universities. I emphasize not just the implementation of models but also the critical importance of Responsible AI. A dedicated chapter explores the ethical dimensions of data, bias in algorithms, and the need for fairness and transparency-skills that are indispensable for the 21st-century engineer.