Python Library & Machine Learning with Agentic AI by Aravind choudhary

Python Library & Machine Learning with Agentic AI is a comprehensive and practical guide that takes readers from Python programming and essential libraries to Machine Learning, Deep Learning, Generative AI, and modern Agentic AI applications. The book begins with Python and its most important libraries, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. Readers learn how to work with data, perform data cleaning and preprocessing, conduct exploratory data analysis, visualize datasets, and develop machine learning models. The Machine Learning section covers supervised and unsupervised learning, feature engineering, model evaluation, optimization, and practical ML workflows. Deep Learning concepts such as neural networks, CNNs, RNNs, and LSTMs provide the foundation for understanding modern AI systems. The book then introduces Generative AI, Large Language Models, prompt engineering, embeddings, Transformers, and Retrieval-Augmented Generation (RAG). Readers learn how to use Python and modern AI frameworks to build intelligent applications. The final section focuses on Agentic AI, covering LangChain, LangGraph, AI agents, tool and function calling, memory, planning, reasoning, state management, 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 Python libraries and Machine Learning to Generative AI and Agentic AI. What You Will Learn Python Programming for AI and Machine Learning Python Modules, Packages, and Libraries NumPy NumPy Arrays and Operations Pandas Series and DataFrames Data Cleaning and Transformation CSV, Excel, JSON, and Database Data Matplotlib Seaborn Data Visualization Exploratory Data Analysis Statistics and Probability Fundamentals Machine Learning Fundamentals Machine Learning Workflow Data Preprocessing Feature Engineering Feature Selection Data Scaling and Encoding 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 Scikit-Learn Model Evaluation Confusion Matrix Accuracy, Precision, Recall, and F1-Score Cross-Validation Hyperparameter Tuning Overfitting and Underfitting Ensemble Learning 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 Vector Databases Semantic Search 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 Management 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 API and Database Integration Web Search and External Tools Autonomous AI Workflows Human-in-the-Loop Systems Multi-Agent Systems Supervisor and Worker Agent Architecture 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 Generative AI Projects Real-World Agentic AI Projects