**Machine Learning & Generative AI with Agentic AI** is a practical, hands-on guide designed to help students, developers, professionals, and AI enthusiasts understand and build modern AI applications.

The book takes you from the fundamentals of **Machine Learning** to advanced concepts in **Generative AI and Agentic AI**, providing a structured learning path with practical examples and real-world applications.

You will learn the foundations of Machine Learning, including data preprocessing, supervised and unsupervised learning, regression, classification, clustering, model evaluation, and practical ML workflows. The book then moves into **Generative AI, Large Language Models (LLMs), Prompt Engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG)**.

The Agentic AI section introduces the concepts behind intelligent AI agents that can reason, use tools, maintain context, make decisions, and perform multi-step tasks. You will explore technologies and frameworks such as **LangChain and LangGraph** for building AI-powered applications and agentic workflows.

### What You Will Learn

* Machine Learning fundamentals and algorithms
* Data preprocessing and model development
* Regression and classification techniques
* Supervised and unsupervised learning
* Model evaluation and practical ML workflows
* Generative AI concepts and applications
* Large Language Models (LLMs)
* Prompt Engineering techniques
* Embeddings and vector databases
* Retrieval-Augmented Generation (RAG)
* AI-powered applications using LangChain
* Agentic AI concepts and architectures
* AI agents, tools, memory, and workflows
* LangGraph for building agentic applications
* Practical AI projects and real-world use cases

Whether you are a **beginner starting your AI journey, a programmer looking to integrate AI into applications, or a professional exploring the latest Generative AI and Agentic AI technologies**, this book provides a practical foundation for developing modern intelligent applications.

By combining **Machine Learning, Generative AI, LLMs, RAG, Prompt Engineering, LangChain, LangGraph, and Agentic AI**, this book provides a comprehensive roadmap for learning and building next-generation AI solutions.