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