About this ebook
Readers will learn the core concepts of supervised, unsupervised, semi-supervised, and reinforcement learning, along with essential topics such as data preprocessing, feature engineering, model training, evaluation techniques, and optimization. The book provides hands-on implementation using Python and popular Machine Learning libraries including NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.
Beyond traditional Machine Learning, this book introduces the emerging field of Agentic AI, where AI systems are designed to perform multi-step reasoning, use external tools, retrieve knowledge, collaborate with other agents, and complete complex real-world tasks autonomously. Readers will explore Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, memory management, planning strategies, multi-agent systems, AI workflows, and AI automation.
Each chapter includes practical examples, coding exercises, real-world case studies, diagrams, and best practices to help readers build production-ready AI applications. By combining Machine Learning with Agentic AI concepts, this book bridges the gap between predictive intelligence and autonomous decision-making systems.
Whether you are preparing for a career in Artificial Intelligence, developing intelligent software, conducting research, or building next-generation AI applications, Machine Learning with Agentic AI provides the knowledge and practical skills needed to succeed in today's rapidly evolving AI landscape.
Key Features
Comprehensive coverage of Machine Learning fundamentals
Python programming for AI and Machine Learning
Data preprocessing, visualization, and feature engineering
Supervised, Unsupervised, and Reinforcement Learning
Deep Learning fundamentals with TensorFlow and PyTorch
Model evaluation, optimization, and deployment
Introduction to Large Language Models (LLMs)
Prompt Engineering techniques and best practices
Agentic AI architecture and intelligent AI agents
Retrieval-Augmented Generation (RAG) and Vector Databases
AI Agents using LangChain, LangGraph, CrewAI, and AutoGen
Multi-Agent Systems and AI orchestration
Memory, planning, reasoning, and tool integration
Real-world projects and industry case studies
Interview questions, coding exercises, and practical implementations
Discover more
About the author
Arvind Choudhary is an experienced Software Developer, Corporate Trainer, and Technology Mentor from India with around 12 years of professional experience in the IT industry. He has worked extensively for 8 years as a developer and 4 years as a corporate trainer, helping students, professionals, and organizations build strong technical skills in modern software technologies.
He specializes in Java Full Stack Development, Python Full Stack Development, MERN Stack, Flutter, Android Development, Artificial Intelligence (AI), Machine Learning (ML), Generative AI, Agentic AI, AI Agents, and Android Firmware Development.
Throughout his career, Arvind Choudhary has trained and mentored numerous students and working professionals through live projects, practical implementation, industry-focused training, and real-world application development. His teaching approach focuses on simplifying complex technical concepts into easy-to-understand practical learning experiences.
As an author and trainer, he is passionate about sharing knowledge in emerging technologies including Generative AI, AI Automation, Prompt Engineering, Large Language Models (LLMs), and modern software engineering practices. His goal is to help learners become industry-ready developers and technology innovators.
Arvind Choudhary continues to contribute to the technology community through technical training, content creation, software development, and educational initiatives focused on next-generation technologies.
