Machine Learning with Agentic AI by Aravind choudhary

Machine Learning with Agentic AI is a comprehensive and practical guide that takes readers from the fundamentals of Machine Learning and Deep Learning to modern Generative AI and Agentic AI applications. The book begins with Python programming, NumPy, Pandas, data preprocessing, data visualization, statistics, and Machine Learning fundamentals. It covers supervised and unsupervised learning, feature engineering, model evaluation, optimization, and practical machine learning workflows. Readers then explore Deep Learning, Neural Networks, NLP, and Transformers, building the foundation required to understand modern AI systems. The book introduces Generative AI and Large Language Models (LLMs), including prompt engineering, embeddings, and multimodal AI. The book then moves into Retrieval-Augmented Generation (RAG), vector databases, LangChain, and LangGraph, followed by Agentic AI concepts such as AI agent architecture, reasoning, planning, memory, tool calling, human-in-the-loop workflows, and multi-agent systems. With practical Python examples, hands-on exercises, real-world use cases, and projects, this book provides a complete learning path from Machine Learning to Generative AI and Agentic AI. What You Will Learn Python Programming for AI NumPy and Pandas Data Cleaning and Preprocessing Exploratory Data Analysis Statistics and Probability Matplotlib and Seaborn Machine Learning Fundamentals Machine Learning Workflow Supervised Learning Linear Regression Logistic Regression K-Nearest Neighbors Decision Trees Random Forest Support Vector Machines Naive Bayes Unsupervised Learning K-Means Clustering Hierarchical Clustering Principal Component Analysis Feature Engineering and Feature Selection Model Evaluation and Cross-Validation Hyperparameter Tuning Overfitting and Underfitting Ensemble Learning Scikit-Learn Model Saving and Deployment Deep Learning Fundamentals Artificial Neural Networks CNN, RNN, and LSTM Natural Language Processing Transformers and Attention Mechanisms Generative AI Fundamentals Large Language Models Tokens and Context Prompt Engineering Zero-Shot, One-Shot, and Few-Shot Prompting Structured Output Embeddings and Semantic Search Vector Databases Retrieval-Augmented Generation (RAG) Document Processing and Text Chunking Retrievers and Vector Search LangChain Chains and Runnables LangGraph Graph-Based AI Workflows Nodes, Edges, and State Conditional Routing and Workflow Loops ToolNode and Tool Calling Function Calling Agentic AI Fundamentals Generative AI vs Agentic AI AI Agent Architecture Reasoning and Decision Making Planning and Task Decomposition Agent Memory Short-Term and Long-Term Memory Concepts External Tools and API Integration Database and Web Tools Autonomous AI Workflows Human-in-the-Loop Systems Multi-Agent Systems Supervisor and Worker Agents Agent Collaboration Error Handling and Recovery Agent Evaluation and Monitoring AI Security and Prompt Injection Tool Permissions and Access Control Hallucination Reduction Responsible AI Hugging Face and LLM APIs AI-Powered Data Analysis Research Agents Document Analysis Agents Coding and Automation Agents AI-Powered Business Workflows AI Application Deployment Real-World Machine Learning Projects Real-World Agentic AI Projects