Program Highlights
6-Month Online Program
Comprehensive curriculum designed for working professionals and students
Industry Expert Instructors
Learn from AI professionals working at leading tech companies
Flexible Monthly Payments
Pay-as-you-go options available for better affordability
Dedicated Discord Community
24/7 discussion group for peer learning and networking
Convenient Schedule
Three weekday evening sessions (1hr) + One weekend session (2hrs)
No High-End Hardware Needed
Cloud-based environments provided for all hands-on exercises
Complete Learning Materials
Access to recordings and resources after each class
Industry-Recognized Certificate
Course completion certificate from Habibaa Technologies Pvt
Student-Friendly Flexibility
Classes can be rescheduled around university exam periods
LLM Learning Path Curriculum
Foundation Stage: Understanding the Basics
Begin your journey with fundamental concepts and essential knowledge about LLMs and generative AI.
1
Introduction to Generative AI and LLMs
- Starting point for complete beginners
- Overview of what generative AI is and how LLMs fit in the landscape
2
How Transformer LLMs Work
- Understanding the core technology behind large language models
- Builds essential vocabulary and concepts for everything that follows
3
Exploring and comparing different LLMs
- Survey of available models and their strengths/weaknesses
- Helps understand the evolving landscape of language models
4
ChatGPT Prompt Engineering for Developers
- Learn how to effectively communicate with LLMs
- Fundamental skill needed before building applications
5
Open Source Models with Hugging Face
- Introduction to practical implementation using popular frameworks
- Exposes you to the ecosystem of available models
Core Concepts Stage: Building Blocks
Deepen your understanding with core technical concepts that form the building blocks of LLM applications.
6
Vector Databases: from Embeddings to Applications
- Understanding how LLMs process and store information
- Critical for retrieval-based applications
7
How Diffusion Models Work
- Broadens understanding to include image generation models
- Important for multimodal applications later
8
Attention in Transformers: Concepts and Code in PyTorch
- Deeper dive into the technical mechanics
- Reinforces understanding from the first course with hands-on code
Development Environment Stage
Learn essential tools and environments for developing and deploying LLM applications.
9
Google Colab
- Introduction to cloud-based notebooks for AI development
- First practical environment for running code without local setup
10
Jupyter
- Local notebook environment for more control
- Important skill for data scientists and AI developers
11
Groq LPU interface
- Introduction to specialized hardware for LLM inference
- Understanding acceleration options for deployment
Application Development Stage: Building Simple Systems
Start building practical applications with LLMs and learn deployment strategies.
12
Building Generative AI Applications with Gradio
- First step into creating interfaces for AI applications
- Quick way to visualize and test concepts
13
LangChain Chat with Your Data
- Introduction to combining LLMs with personal data
- Foundation for more advanced RAG systems
14
Serverless LLM Apps Amazon Bedrock
- Learn to deploy applications in production environments
- Introduction to cloud-based LLM services
Advanced Applications Stage: Complex Systems
Develop more sophisticated applications with advanced techniques like RAG, finetuning, and agentic systems.
15
Building and Evaluating Advanced RAG
- Building on the LangChain course with more sophisticated techniques
- Introduces evaluation, a critical skill for improvement
16
Finetuning Large Language Models
- Customizing models for specific use cases
- Enhances performance beyond prompt engineering alone
17
Functions, Tools and Agents with LangChain
- First introduction to agentic systems
- Prepares for more complex agent architectures
Expert Stage: Multi-Agent Systems & Evaluation
Master cutting-edge concepts in AI agent development, multimodal applications, and system evaluation.
18
AI Agents in LangGraph
- More advanced agent frameworks
- Introduces flow-based agent design
19
Evaluating AI Agents
- Critical skills for measuring agent performance
- Important before building complex multi-agent systems
20
Multi AI Agent Systems with crewAI
- Building systems with multiple specialized agents
- Advanced orchestration techniques
21
Building Agentic RAG with Llamaindex
- Combining agents with sophisticated retrieval systems
- Advanced application of earlier concepts
22
Multimodal RAG: Chat with Videos
- Extending RAG beyond text to include video
- Cutting-edge application combining multiple concepts
23
Build Apps with Windsurf's AI Coding Agents
- Culmination course applying agents to code generation
- Practical application of all previous concepts
Technologies You'll Master
OpenAI
LangChain
crewAI
Groq
LlamaIndex
Hugging Face
LangGraph
Amazon Bedrock