Program Curriculum

    1. Important Links for The Program

    2. Orientation Session (28th June)

    3. GenAI Program Schedule

    4. Understanding the key terms under AI Umbrella

    1. Introduction to AI

    2. Introduction to AI (Slides)

    3. The Evolution of Transformers to Large Language Models (LLMs)

    4. Introduction to Generative AI (GenAI)

    5. Understanding Key Concepts and Terminologies in Generative AI

      FREE PREVIEW
    6. Steps in Generative AI Pipeline

      FREE PREVIEW
    7. 4 important steps in Generative AI Pipeline

    8. Steps to access OpenAI Playground

    9. OpenAI API Usage Guide

    10. OpenAI API Usage Notebook Guide

    11. LLM model selection criteria and framework

    12. Project 1: AI Support Ticket Triage System

    13. Transformers, OpenAI API & Fine-tuning basics

    1. Open-Source vs. Closed-Source Models in Generative AI

    2. Basic Prompt Engineering Techniques

    3. Introduction to Vector Embeddings

    4. Hugging Face Models : An Introduction

    5. Hugging Face Datasets : An Introduction

    6. How to use BERT for simple tasks

    7. Demo : Hugging Face AutoModel

      FREE PREVIEW
    8. HuggingFace Exercise

    9. Quantization

    10. Customer Support Ticket Tagging

    11. Advanced Fine-tuning Techniques

    12. Finetuning Llama 3.2 for Customer Support

    13. Merge LoRa adapter model with Base model

    14. Week 2: Session Recording

    15. Optimization Methods for LLMs (PPO, DPO)

    16. DPO optimized Fine-Tuning for Llama 3.2 1B for Organizational Security Protocols

    17. GRPO Method for Fine-Tuning Local LLMs

    18. Streamlit Session

    19. Project 2: Fine-Tuning a Hugging Face Model for Customer Support or Organizational Use Case

    20. End-to-End LLM Fine-Tuning

    1. Introduction to RAG

    2. Retrieval Augmented Generation

    3. Vector Database

    4. Introduction to LangChain

    5. Introduction to Langchain

    6. Personal Resource Assistant using Langchain

    7. Retrieval Optimization Techniques

    8. Hybrid Search and Re-ranking Optimization

    9. Memory in LLMs

    10. Week 3: Discussion on RAG Architectures, Designs and Methods

    11. Week 3: Live Session PPT

    12. Finetuning a Vector Embeddings Model

    13. Introduction to LangGraph with Qdrant vector database and Cross-encoder Re-ranking models

    14. Evaluating RAG results

    15. Project 3: Building an Optimized RAG Pipeline for Legal Query Resolution

    16. Current State of RAG

    17. Building an Optimized RAG Pipeline for Legal Query Resolution

    1. AI Agents : An Introduction

    2. Introduction to Agents

    3. Core Components of an AI Agent

    4. Email Generating Agent with CrewAI

    5. Tools in AI Agents

    6. End to End Architecture of a Single Agent

    7. What is a Multi-Agent System?

    8. Multi AI Agent Blog Generator using CrewAI

    9. LangChain Agents

    10. LangGraph Agents

    11. Designing Stateful AI Agents using LangGraph

    12. Agentic RAG with Phidata

    13. Serving AI Agent with FastAPI

    14. Router in Agentic RAG

    15. Human-in-Loop workflows for Agents

    16. Corrective RAG (CRAG)

    17. Market Research Copilot

    18. Building Conversational Business Intelligence Agent

    19. Project 4: Conversational BI Agent

    20. Agent Evaluation and Observability

    21. Build and Deploy Your First MCP Server

    22. Project 5: Build Your Own Agentic AI Solution

    23. Building a Multi-Agent Document Drafter

    1. LLM Model Serving

    2. Hugging Face Inference Endpoints

    3. GGUF (GPT-Generated Unified Format)

    4. Llama.cpp -> Conversion from .safetensors to .gguf

    5. Ollama: Running Large Language Models Locally with Ease

    6. LLM Evaluation

    7. LLM Evaluation PPT

    8. vLLM: Enabling faster inference and serving for LLMs

    9. Docker and Docker Compose

    10. AWS Deployment

    11. Cerebrium: Serverless Model Deployment

    12. AWS Bedrock

    13. Deploying a GenAI Application

About this course

  • $99.00
  • 100 lessons

Program Instructor

Saurabh Shahane

Lead Instructor

With over 6+ years of professional experience, Saurabh has established expertise in building and delivering industry and research-driven Data Science and AI projects across a variety of domains. A Kaggle Grandmaster and trusted consultant to leading enterprises and startups, Saurabh has played a pivotal role in enabling organizations to adopt and scale Data Science and AI solutions effectively. Through courses, workshops, and mentorship programs, he has impacted the careers of over 10,000 professionals, fostering a supportive and knowledge-driven learning environment. Saurabh created this program to champion hands-on, project-based learning, preparing participants to confidently apply AI concepts in real-world scenarios and drive meaningful outcomes.

Words from our learners

“I have registered myself for the Guided Projects of Deep Learning because I want to work on real-time DL projects and to work in a team with a deadline, here The Machine Learning Company has provided exactly what I needed. I wanted to try something more and from this, I have gained skills like end-to-end ML Pipeline, Streamlit integration for any project, deployment, AI explainable, MLOps, and were part of many discussions. I do recommend being part of it and keep learning."”

Sandeep Kirwai, Data Scientist II at Pattern

“MGP program from TMLC was an amazing experience in itself. I came here to learn Explainable AI and brush up my Deep learning skills, I thoroughly enjoyed the course. The team of mentors are great at tutoring. This was a totally worthy course which I will recommend for anyone willing to enter the Data Science Field by building projects on real world business use cases.If you are consistent you'll get the BEST out of this course! Thanks a lot to team TMLC for such an amazing program."”

Vetrivel PS, Lead NLP Engineer, Genpact

“"This program introduced me to many new parts of deep learning like Explainable AI, MLOps and deployment which I didn’t have any Idea about before. I’m sure this helps me a lot in boosting my confidence on Deep learning models as well as improving my team working skills. TMLC’s guided project is the perfect place to have a head start on deep learning and its applications in various fields"”

Kabilan N, Computer Vision Engineer, The ePlane Company

Key aspects of the program

While crafting this program, our focus was the end to end application development.

  • Build 6+ end to end Generative AI Applications. Focused on LLM Fine-tuning, Optimization, RAG, Vector DBs, LLM Evaluation, Deployment and LLMOps.

  • Industry grade datasets and usecases that will build a problem solving perspective.

  • Hands on experience with 15+ LLM driven tools that are highly relevant to today's industry.

Master the art of building GenAI applications.