AI & GENERATIVE AI COURSE IN CHENNAI

About This AI Course in Chennai

This comprehensive AI & Generative AI course in Chennai covers the entire AI spectrum – from foundational Python programming and Machine Learning to advanced Generative AI technologies including LLMs, Transformers, LangChain, Hugging Face, Vector Databases, RAG systems, AI Agents, and Model Deployment. You'll build 12+ production-ready AI applications and master the skills that top companies are desperately hiring for.

Syllabus Overview

• Module 1: Python for AI & Data Science
  • Python Fundamentals & Best Practices
  • NumPy for Numerical Computing
  • Pandas for Data Manipulation
  • Matplotlib & Seaborn for Visualization
  • Working with APIs & JSON Data
  • File Handling & Data Processing
  • Virtual Environments & Package Management
  • Object-Oriented Programming in Python
  • Error Handling & Logging
• Module 2: Machine Learning Fundamentals
  • Introduction to Machine Learning
  • Supervised vs Unsupervised Learning
  • Regression (Linear, Polynomial, Ridge, Lasso)
  • Classification (Logistic Regression, SVM, Decision Trees)
  • Random Forest & Gradient Boosting (XGBoost)
  • Clustering (K-Means, Hierarchical, DBSCAN)
  • Dimensionality Reduction (PCA, t-SNE)
  • Model Evaluation & Cross-Validation
  • Feature Engineering & Selection
  • Hyperparameter Tuning
  • ML Pipeline with Scikit-learn
• Module 3: Deep Learning & Neural Networks
  • Neural Networks Fundamentals
  • Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
  • Forward & Backpropagation
  • Loss Functions & Optimizers
  • Deep Neural Networks with TensorFlow/Keras
  • Convolutional Neural Networks (CNNs)
  • Transfer Learning & Pre-trained Models
  • Recurrent Neural Networks (RNNs, LSTM, GRU)
  • Regularization (Dropout, Batch Normalization)
  • PyTorch Fundamentals
  • GPU Training & Optimization
• Module 4: Natural Language Processing (NLP)
  • Text Preprocessing & Tokenization
  • Word Embeddings (Word2Vec, GloVe, FastText)
  • Bag of Words & TF-IDF
  • Named Entity Recognition (NER)
  • Sentiment Analysis
  • Text Classification
  • Sequence-to-Sequence Models
  • Attention Mechanisms
  • NLP with spaCy & NLTK
  • Text Generation Basics
• Module 5: Transformers Architecture
  • Why Transformers Replaced RNNs/LSTMs
  • Self-Attention Mechanism Deep Dive
  • Multi-Head Attention
  • Positional Encoding
  • Encoder-Only Architecture (BERT)
  • Decoder-Only Architecture (GPT)
  • Encoder-Decoder Architecture (T5, BART)
  • Layer Normalization & Residual Connections
  • Training Transformers
  • Computational Complexity & Optimizations
  • Implementing Attention from Scratch
• Module 6: Large Language Models (LLMs)
  • Evolution of Language Models
  • GPT-3, GPT-3.5, GPT-4 Architecture
  • Open-Source LLMs (Llama 2/3, Mistral, Falcon)
  • Gemini & Claude Architecture
  • Tokenization (BPE, WordPiece, SentencePiece)
  • Context Windows & Positional Interpolation
  • LLM Training (Pre-training, SFT, RLHF)
  • Inference Optimization (Quantization, KV Cache)
  • Temperature, Top-K, Top-P Sampling
  • Hallucinations & Mitigation Strategies
  • Evaluating LLM Performance
  • Cost Optimization for LLM APIs
• Module 7: Hugging Face Ecosystem
  • Hugging Face Hub & Model Repository
  • Transformers Library Deep Dive
  • Pipelines for Quick Inference
  • AutoClasses (AutoModel, AutoTokenizer)
  • Loading & Using Pre-trained Models
  • Fine-tuning Models with Trainer API
  • Tokenizers Library
  • Datasets Library
  • Model Cards & Documentation
  • Hugging Face Spaces for Deployment
  • Gradio & Streamlit Integration
  • Running Models Locally
• Module 8: LangChain & LLM Orchestration
  • LangChain Architecture & Components
  • Prompt Templates & Management
  • Chains (Simple, Sequential, Router)
  • Output Parsers (JSON, Pydantic, Structured)
  • Memory (Buffer, Summary, Conversation)
  • Document Loaders (PDF, CSV, Web, Database)
  • Text Splitters & Chunking Strategies
  • Embeddings & Vector Stores
  • Retrievers & RAG Pipelines
  • LCEL (LangChain Expression Language)
  • Building AI Chatbots with LangChain
  • LangSmith for Debugging & Monitoring
• Module 9: Vector Databases & Embeddings
  • Understanding Embeddings & Vector Spaces
  • OpenAI Embeddings (ada-002, text-embedding-3)
  • Open-Source Embedding Models
  • Semantic Search Fundamentals
  • Similarity Metrics (Cosine, Euclidean, Dot Product)
  • Approximate Nearest Neighbors (ANN)
  • Pinecone Setup & Configuration
  • ChromaDB for Local Development
  • Weaviate & Qdrant
  • FAISS for Efficient Search
  • Chunking Strategies & Overlap
  • Metadata Filtering & Hybrid Search
• Module 10: RAG (Retrieval-Augmented Generation)
  • RAG Architecture & Components
  • Why RAG? (Hallucination Reduction, Fresh Data)
  • Building a Basic RAG Pipeline
  • Advanced RAG Techniques
  • Multi-Modal RAG (Text + Images)
  • Self-Querying Retrieval
  • Re-Ranking & Result Fusion
  • Context Injection Strategies
  • Evaluation Metrics (Faithfulness, Relevance)
  • RAG vs Fine-tuning: When to Use What
  • Production RAG Architecture
  • Building an Enterprise Document Q&A System
• Module 11: Generative AI Applications
  • Text Generation & Summarization
  • Code Generation & Code Assistance
  • Image Generation (DALL-E, Stable Diffusion, Midjourney)
  • Video Generation & Understanding
  • Speech-to-Text & Text-to-Speech (Whisper, ElevenLabs)
  • Multi-Modal AI Applications
  • AI-Powered Search Engines
  • Content Creation & Marketing AI
  • AI for Data Analysis & Visualization
  • Fine-tuning for Domain-Specific Tasks
  • LoRA & QLoRA for Efficient Fine-tuning
• Module 12: AI Agents & Autonomous Systems
  • Introduction to AI Agents
  • Agent Architectures (ReAct, Plan-and-Execute)
  • Tool Use & Function Calling
  • Building Agents with LangChain
  • Multi-Agent Systems & Collaboration
  • AutoGPT & BabyAGI Concepts
  • Agent Memory & Planning
  • Code Interpreter & Data Analysis Agents
  • Web Browsing & Research Agents
  • Safety & Guardrails for Agents
  • Deploying Agents in Production
• Module 13: MLOps & AI Deployment
  • MLOps Fundamentals & Best Practices
  • Model Versioning & Experiment Tracking
  • Docker for AI Applications
  • Deploying Models as REST APIs (FastAPI)
  • Cloud Deployment (AWS SageMaker, GCP Vertex AI)
  • Serverless AI with Cloud Functions
  • CI/CD for ML Pipelines
  • Model Monitoring & Drift Detection
  • A/B Testing for AI Models
  • Scaling AI Applications
  • Cost Optimization in Production
• Module 14: Capstone Projects
  • Enterprise Document Q&A System with RAG
  • Multi-Agent AI Research Assistant
  • AI-Powered Code Review Assistant
  • Customer Support Chatbot with History
  • Multi-Modal Content Generation Platform
  • Financial Document Analyzer with GenAI
  • AI Agent for Automated Data Analysis
  • Production-Ready RAG API Service

Tools & Frameworks Covered

Hands-On AI Projects in Chennai

• Enterprise Document Q&A System with RAG & LangChain
• Multi-Agent AI Research Assistant with Tool Use
• AI-Powered Code Review & Generation Assistant
• Intelligent Customer Support Chatbot with Memory
• Multi-Modal Content Generation Platform (Text + Image)
• Financial Document Analyzer with GenAI & RAG
• AI Agent for Automated Data Analysis & Reporting
• Production-Ready RAG API with FastAPI & Docker
• Fine-tuned LLM for Domain-Specific Tasks
• Real-Time AI Voice Assistant with Whisper & TTS
• Automated ML Pipeline with MLOps Best Practices
• End-to-End AI SaaS Application

Career Outcomes in Chennai

• AI/ML Engineer₹8 – ₹20 LPA
• Generative AI Engineer₹12 – ₹30 LPA
• LLM / NLP Engineer₹10 – ₹25 LPA
• AI Research Scientist₹15 – ₹35 LPA

Meet Your Chennai-Based Trainer

Muthu

AI Research Scientist & GenAI Expert 12+ Years Exp.

PhD in Machine Learning from IIT Delhi. Former AI Lead at Google Research India. Built production RAG systems and LLM applications serving millions of users. Published 20+ papers on NLP and Transformers. Chennai-based mentor with real MNC experience — not just theory.

Frequently Asked Questions

• Which is the best AI training institute in Chennai?

  Course Bazaar is widely recognised as a top AI and Generative AI training institute in Chennai, offering job-ready courses with live projects, internships, and strong placement support.

• What's the difference between this and a regular AI course?

  This course covers both traditional AI/ML and cutting-edge Generative AI (LLMs, RAG, AI Agents). You'll learn to build modern AI applications that companies are actively hiring for right now.

• Do I need a GPU for the hands-on projects?

  No. We use cloud-based GPUs (Google Colab Pro, Hugging Face Spaces, AWS SageMaker) for all training and inference. A standard laptop is sufficient for the entire course.

• Will I learn about OpenAI, Gemini, and Claude?

  Yes! You'll work with OpenAI GPT-4, Google Gemini, Anthropic Claude, and open-source models like Llama 3 and Mistral. You'll learn when and how to use each one effectively.

• Is this course suitable for beginners?

  Basic Python knowledge is recommended. The course includes a Python refresher module and starts from ML fundamentals before progressing to advanced GenAI topics.

• Does Course Bazaar offer placement assistance after AI training?

  Yes, we provide 95% placement assistance with 600+ hiring partners, including TCS, Infosys, Zoho, and Freshworks.