Python Library & Machine Learning with Agentic AI Programming: Master Python Libraries, Machine Learning, Generative AI, LLMs, and Agentic AI with Practical Projects

The book begins with Python programming fundamentals and introduces essential libraries such as NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn, TensorFlow, PyTorch, OpenCV, NLTK, and LangChain. Readers will learn how these libraries are used for data analysis, visualization, machine learning, natural language processing, computer vision, and AI application development.

It then explores the core concepts of Machine Learning, including data preprocessing, feature engineering, supervised learning, unsupervised learning, model evaluation, hyperparameter tuning, and deployment. Practical examples and real-world projects help readers understand algorithms such as Linear Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Clustering, and Neural Networks.

A major highlight of this book is its in-depth coverage of Agentic AI, the next generation of intelligent systems capable of reasoning, planning, memory management, and autonomous decision-making. Readers will learn how to design and build AI agents using modern frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LiteLLM, MCP (Model Context Protocol), and Retrieval-Augmented Generation (RAG). The book also explains AI workflows, multi-agent systems, tool integration, vector databases, prompt engineering, and AI orchestration.

The book includes numerous hands-on examples, coding exercises, interview questions, and real-world case studies to strengthen practical skills. Whether your goal is to become a Machine Learning Engineer, AI Developer, Data Scientist, or Agentic AI Engineer, this book provides the knowledge and practical experience needed to build modern AI-powered applications.

Key Features

Comprehensive coverage of Python programming and essential Python libraries

Hands-on Machine Learning concepts with real-world datasets

Practical implementation using Scikit-learn, TensorFlow, and PyTorch

Data preprocessing, visualization, feature engineering, and model evaluation

Deep learning fundamentals and neural network implementation

Complete introduction to Agentic AI and autonomous AI systems

LangChain, LangGraph, CrewAI, AutoGen, LiteLLM, MCP, and RAG implementation

Prompt Engineering and AI workflow automation

Vector databases, embeddings, memory, and tool calling

End-to-end AI projects with step-by-step explanations

Interview questions, coding exercises, and best practices

Suitable for beginners, students, developers, researchers, and AI professionals

This book serves as a complete learning resource for mastering Python Libraries, Machine Learning, and Agentic AI Programming, enabling readers to confidently develop intelligent, scalable, and production-ready AI applications for today's rapidly evolving technology landscape.