Machine Learning with Agentic AI

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