Module 1: Development Environment Setup

Build a professional AI development environment by installing and configuring Python, Jupyter Notebook, Visual Studio Code, PyCharm, virtual environments, package managers, and essential development tools. Learn project organization, dependency management, and best practices for developing scalable AI applications.

Module 2: Introduction to AI and Computing

Understand the core concepts of computing, instruction execution, computer capabilities, and Intelligence as a Service (AIaaS). Build the foundation required to understand how modern Artificial Intelligence systems operate.

Module 3: Introduction to Artificial Intelligence

Explore Artificial Intelligence fundamentals, different types of AI, real-world applications, advantages, limitations, and future trends. Learn how AI is transforming industries through intelligent automation and decision-making.

Module 4: Introduction to Machine Learning

Understand Machine Learning concepts, supervised and unsupervised learning, training workflows, model development, and practical applications used across modern AI solutions.

Module 5: Introduction to Neural Networks

Learn how Artificial Neural Networks (ANNs) work, understand their architecture, training process, applications, and how they differ from traditional programming techniques.

Module 6: Introduction to Deep Learning

Master Deep Learning fundamentals, neural network architectures, multimodal AI, and understand how deep learning powers today’s Generative AI applications.

Module 7: Generative AI & Large Language Models (LLMs)

Gain comprehensive knowledge of Generative AI and Large Language Models, including tokenization, inference, GPT evolution, context windows, vocabulary, and real-world LLM applications.

Module 8: Transformer Architecture & Embeddings

Understand the Transformer architecture, embeddings, attention mechanisms, positional encoding, cosine similarity, and the technologies powering today’s state-of-the-art language models.

Module 9: Working with Large Language Models (LLMs)

Learn to work with commercial and open-source LLMs, connect models through APIs, understand inference, generation parameters, and integrate AI models into applications.

Module 10: Prompt Engineering

Master Prompt Engineering techniques for building reliable AI applications, including prompt chaining, guardrails, structured outputs, JSON generation, code generation, and advanced prompting strategies.

Module 11: Self-Hosted LLMs with Ollama

Deploy and manage local AI models using Ollama. Learn model installation, API integration, Python connectivity, and self-hosted AI deployment without cloud dependencies.

Module 12: Building an LLM as a Service with Flask

Develop AI-powered web applications using Flask by building chat interfaces, REST APIs, LLM integrations, frontend connectivity, testing, and deployment workflows.

Module 13: LLM Service Testing with Postman & Bruno

Learn professional API testing using Postman and Bruno by validating AI endpoints, handling requests and responses, debugging errors, and ensuring production-ready API quality.

Module 14: Building LLM Services with FastAPI

Build scalable AI APIs using FastAPI with request validation, Pydantic models, interactive documentation, asynchronous programming, and enterprise-ready backend development.

Module 15: Hugging Face & Open-Source LLMs

Explore Hugging Face, Transformers, Tokenizers, Datasets, Pipelines, Spaces, model architectures, licensing, and open-source AI model deployment.

Module 16: Advanced LLM Features

Implement advanced LLM capabilities including structured outputs, JSON responses, streaming, multimodal AI, function calling, and vision-enabled AI applications.

Module 17: Agentic AI

Learn how intelligent AI agents plan, reason, observe, make decisions, call external tools, and autonomously solve complex business problems using modern Agentic AI frameworks.

Module 18: LangChain

Master LangChain to build sophisticated LLM applications using prompt templates, chains, memory, document loaders, tools, callbacks, and AI agents.

Module 19: Memory in AI Agents

Understand AI memory architectures including conversation memory, persistent memory, session management, checkpoints, and long-term context handling.

Module 20: Retrieval-Augmented Generation (RAG)

Build enterprise-grade RAG systems using document loaders, embeddings, chunking, retrievers, vector search, hybrid search, and intelligent knowledge retrieval.

Module 21: Vector Databases

Learn how vector databases store embeddings, perform similarity search, manage metadata, execute CRUD operations, and support semantic AI applications.

Module 22: LangGraph

Design advanced AI workflows using LangGraph by implementing nodes, edges, state management, conditional routing, memory, checkpoints, and human-in-the-loop systems.

Module 23: Model Context Protocol (MCP)

Understand the Model Context Protocol (MCP), build MCP servers and clients, create AI tools, manage resources, and integrate MCP with intelligent AI agents.

Module 24: Multi-Agent Systems

Build collaborative AI systems where multiple intelligent agents communicate, coordinate, share tools, manage tasks, exchange memory, and solve enterprise-scale workflows.

Module 25: AI Evaluation

Evaluate AI applications using precision, recall, hallucination detection, latency analysis, prompt evaluation, RAG benchmarking, cost optimization, and agent performance metrics.

Module 26: Deployment & Production AI Applications

Deploy production-ready AI applications using FastAPI, Docker, Docker Compose, Nginx, HTTPS, logging, monitoring, authentication, environment management, and end-to-end AI deployment best practices while completing real-world industrial projects