Machine Learning with Generative AI is a comprehensive and practical guide that takes readers from the fundamentals of Machine Learning to modern Generative AI and intelligent AI applications.

The book begins with Python programming, data analysis, data preprocessing, statistics, and machine learning fundamentals, followed by supervised and unsupervised learning algorithms, model evaluation, feature engineering, and optimization techniques.

It then introduces Deep Learning and Neural Networks, covering ANN, CNN, RNN, LSTM, and other important concepts required for modern AI development.

The book gradually moves into Generative AI and Large Language Models (LLMs), explaining tokens, embeddings, prompt engineering, transformers, text generation, multimodal AI, and LLM-based applications. Readers will also learn how to build practical applications using RAG, vector databases, LangChain, LangGraph, AI Agents, and tool calling.

With practical examples, programming 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 and Machine Learning
NumPy and Pandas
Data Cleaning and Preprocessing
Exploratory Data Analysis
Statistics and Probability
Data Visualization with Matplotlib and Seaborn
Machine Learning Fundamentals
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
Feature Selection
Data Scaling and Encoding
Model Evaluation
Confusion Matrix
Accuracy, Precision, Recall, and F1-Score
Cross-Validation
Hyperparameter Tuning
Overfitting and Underfitting
Ensemble Learning
Scikit-Learn
Model Saving and Deployment
Deep Learning Fundamentals
Artificial Neural Networks
CNN and Image-Based Applications
RNN and Sequence Models
LSTM Networks
Natural Language Processing
Transformers
Attention Mechanisms
Generative AI Fundamentals
Large Language Models
Tokens and Context Windows
Model Parameters
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
LangGraph
AI Agents
Tool and Function Calling
Agentic AI
Memory and State Management
Human-in-the-Loop Workflows
Multi-Agent Systems
Hugging Face and LLM APIs
Multimodal Generative AI
Chatbots and Question-Answering Systems
AI-Powered Data Analysis
AI-Based Document Processing
Model Evaluation and Monitoring
Hallucination Reduction
AI Security and Prompt Injection
Responsible AI
AI Application Deployment
Real-World Machine Learning and Generative AI Projects