GenAI Learning Week

Free 7-Day Learning Marathon!

Advanced Curriculum with 200+ Hours of Learning
Master 26+ GenAI Tools and Libraries
Insights from Top Generative AI Experts

200+ Hrs

Comprehensive Learning

10+

Real-world Projects

100% Scholarship Offer*

Claim Your Access to 7 Days of FREE GenAI Learning!

Personalized Roadmap

Navigate Your GenAI Journey with a Customized Learning Experience!

1Introduction to Gen AI
2Human decision making and its biases
3Structured approach to problem solving
4Applied ML, DL and NLP
5Getting started with LLMs
6LLM applications with prompt engineering
7Introduction to LangChain
8Building production ready RAG systems
9Fine-tuning LLMs
10Training LLMs from scratch
11Mastering Stable Diffusion- beginner to advanced
12Generative AI for business- strategies and Responsible AI

Skill Up with Our Cutting-Edge Curriculum

19+ modules, crafted by experts, to rocket you from data whispers to LLM mastery. Conquer Stable Diffusion, dominate Reinforcement Learning and speak the language of AI.

20+ Projects

200+ Hours

26+ Tools

15+ Assignments

Introduction to Generative AI

Why Decision Making is Hard
Data in Decision Making
Group Decision Making - Perceptions, Prejudices and Biases
Group Decision Making - Role of Context, Hierarchy and Emotional Dynamics

Strtuctured Thinking
Defining problem statements
Frameworks of Problem Solving
Finalizing the Solution
Human-Centered Design

Introduction to Data Science and Machine Learning
Introduction to Python
Modules, Packages and Standard Libraries
Machine Learning Lifecycle
Project: Customer Churn Prediction

Introduction to Deep Learning
Understanding the working of Neural Networks
Improving Deep Neural Networks
Guided Project

Introduction to NLP
Building a basic classification model
NLP: Recurrent Neural Network
Attention mechanism and Transformers
Preparing for LLMs

Introduction
The Evolution of NLP
What are Large Language Models?
The Current State of the Art in LLMs
Generative AI - Glossary

How to build diffferent LLM AppIications?
Getting Started with Prompt Engineering
Best Practices and Guidelines for Prompt Engineering
Prompt Engineering with Open Source LLMs
Building Chatbot on Your Enterprise Data

Thought Structures
Advanced Prompting and Verification

Introduction to Generative AI applications
No-code Generative AI app Development
Code-focused Generative AI App Development

Introduction to the LangChain Ecosystem
LangChain Then vs. Now - Understanding the Subtle Differences
Core Components of LangChain
LangChain: AI Agents and Agentic Systems
LangChain: Deployment and Monitoring

Introduction to RAG systems
Getting Started with LlamaIndex
Components of LlamaIndex
Customization in LlamaIndex
Advanced approaches for powerful RAG system

Introduction to LLM-Based Agents and Basic Agent Design
Building Agents with LangChain
Collaborative Agents with AutoGen
Stateful Workflows with LangGraph

Overview of LLMOps
AWS SageMaker for Data Scientists
LangChain for Continuous Integration
SageMaker Pipelines
Kubernetes for Continuous deployment

Introduction to Reinforcement Learning
Markov Decision Processes
Monte Carlo and Temporal-Difference Methods
Proximal Policy Optimization ( PPO ) and DDPG
Techniques and Algorithms for RLHF and DPO

Introduction to the Course
Introduction to Finetuning LLMs
Instruction Finetuning in Practice
Parameter-Efficient Finetuning (PEFT)
Popular PEFT methods in practice

Getting Started with Training LLMs from Scratch
Important Concepts for Training LLMs from Scratch
Steps involved in Training LLMs from Scratch
Training your own LLMs from Scratch
Aligining LLM with Human Preferences

Introduction to Computer Vision
Building Blocks for Image Recognition

Introduction to Stable Diffusion
Understanding Core Components of Stable Diffusion
Understanding Variational Autoencoders
In depth understanding of stable diffusion
Implement DDPM from scratch

Understanding Dalle 2
Steps involved in training stable diffusion
Mastering stability.ai and its tools
Prompt Engineering Concepts for Stable Diffusion

Img2img
CIVITAI
MidJourney
InstructPix2Pix Paper review and ControlNet

Introduction to Responsible AI in the Generative AI Era

Defining Success in Artificial Intelligence
The Synergy of AI, Engineering & Design 
Errors in AI
Building Organizational Effectiveness in AI
Strategies for Successful AI Implementation

Introduction
Generative AI - The New Electricity
Enterprising Generative AI
Drive to succeed
All you need to know

GenAI Scholarship Offer

100% Scholarship* (Excluding Taxes)

All Courses including capstone project must be completed within 9 months of enrollment.
100% fees will be refunded excluding payment gateway fees (up to 10%).

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10+ Generative AI Projects to Put Theory into Practice

Dive into real-world challenges, from harnessing ChatGPT to building conversational chatbots and RAG systems.

Learning Objective:

Train and evaluate LLMs from scratch
Learn LLM best practices and setup
Implement advanced computing strategies

Training Large Language Models

Build Large Language Models (LLMs) like GPT-3.5 from scratch

Learning Objective:

Master building a ChatGPT-like LLM
Apply pretraining, finetuning, RLHF
Learn dialogue-optimized LLM practices

ChatGPT Model Building

Develop a personalized ChatGPT model, starting from the basics up

Learning Objective:

Create a RAG-based QA Chatbot
Develop apps end-to-end with LangChain and Streamlit
Integrate app UI and backend seamlessly

Building end-to-end RAG Apps

Craft RAG-based chatbots and Full-stack Applications with Integrated Frontend-backend synchronization

Learning Objective:

Construct Conversational Bots with LLMs including ChatGPT
Develop AI Instruments and Agents via LangChain
Establish and Manage LLM Applications using LangChain

Build Conversational Apps and Agents

Create advanced conversational interfaces and intelligent Agents with LLMs and LangChain Technology

Learning Objective:

Enhance search accuracy in RAG systems through reranking
Apply RAG system techniques from cutting-edge studies
Construct RAG systems for diverse data types including tables, text, and images

Advanced RAG System Development

Master precision in RAG systems across various data formats with State-of-the-art Techniques

Learning Objective:

Master prompt engineering techniques
Build chatbots using ChatGPT API
Implement LLMs on private data

Prompt-Driven LLM Apps

Develop your own LLM Application using Prompt Engineering

Learning Objective:

Build RAG systems using LlamaIndex
Explore advanced LlamaIndex components
Fine-tune embeddings and retrieval

RAG System Development

Create a production ready RAG systems on your private data

Learning Objective:

Efficient LLM finetuning with PEFT
Apply LoRA, QLoRA, soft prompting
Build instruction-following LLMs

LLM PEFT Finetuning

Finetuning LLMs using Soft Prompting, Adaptor techniques using PEFT

Learning Objective:

Fine-tune Stable Diffusion for datasets
Apply best practices in customization
Understand Stable Diffusion intricacies

Customized Diffusion Model Tuning

Finetune your own Stable Diffusion Models on custom dataset

Learning Objective:

Build Text to Image models with DreamBooth
Implement DreamBooth on personal datasets
Create context-specific visual models

DreamBooth Image Creation

Build your own personalized Text to Image models using DreamBooth

Learning Objective:

Fine-tune diffusion models with ControlNets
Optimize InstructPix2Pix in diffusion models
Tailor models for specific datasets

Diffusion Model Refinement

Finetune Diffusion models using ControlNets and InstructPix2Pix models

Learn from Seasoned Pros

World-Class Instructors Sharing Industry Secrets & Best Practices!

Mustafa Kadioglu is a prominent figure in the field of data science and artificial intelligence. He currently serves as a Lead Data Scientist and AI/ML Engineer at Cisco, where he has developed expertise in Python, data analysis, machine learning, and natural language processing (NLP)

Mustafa Kadioglu

Lead Data Scientist

Srikanth Velamakanni is the Co-founder, Group Chief Executive and Vice Chairman of Fractal. Fractal is one of the most prominent providers of Artificial Intelligence to Fortune 500®companies.

Srikanth Velamakanni

Co-Founder, Group Chief Executive and Vice Chairman

Sourab Mangrulkar, with a specialization in ML and Deep Learning from NIT Goa, has worked at Microsoft, Amazon, and Hugging Face, focusing on diverse AI challenges and contributing to open-source projects like Accelerate and PEFT.

Sourab Mangrulkar

ML Engineer & Applied Scientist

Sandeep Singh, expert senior director at Bain & Company is a leader in AI and Computer Vision. He has pioneered advanced geospatial solutions in Silicon Valley, enhancing mapping, navigation, and sector-wide applications.

Sandeep Singh

Expert Senior Director

Dipanjan has over 10+ years of hands-on and leadership industry experience as well as training, consulting and education initiatives in Data Science and Artificial Intelligence.

Dipanjan S.

Head of Community and Principal AI Scientist

Bhaskarjit is an award-winning data scientist with a diverse background in multiple domains such as Retail, Airlines, Media & Entertainment, BFSI

Bhaskarjit Sarmah

Vice President

Ravi is a Developer Advocate Enginneer at LlamaIndex. His involvement in the field of AI spans many years, marked by notable contributions in Natural Language Processing (NLP) and recommender systems.

Ravi Theja

Developer Advocate

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- world's 2nd largest Data Science coummunity.

Kunal Jain

Founder & CEO

Aravind Pai, Senior Data Scientist at Analytics Vidhya, specializes in Generative AI, Deep Learning, Computer Vision, and NLP. He has developed impactful AI technologies across various sectors including sports and healthcare.

Aravind Pai

Senior Data Scientist

Mani Kanteswara Rao Garlapati is an Associate Principal at Google, where he leads data science initiatives focused on fraud and spam detection across various Google products. With a strong background in machine learning and data science, he has previously held positions such as Lead Strategist at Google and Senior Data Scientist at Walmart Labs

Mani Garlapati

Associate Principal

Sumit Jain is a seasoned professional in the fields of artificial intelligence and data science, currently working at Microsoft in the Data & Applied Sciences division. He specializes in real-time generative AI at scale and provides technical leadership and advisory services

Sumit Jain

Data & Applied Sciences

Shahebaz Mohammad is a renowned Kaggle Grandmaster and LinkedIn Top ML Voice who currently works as a Lead Applied Machine Learning Engineer at Snorkel AI. He has established himself as an expert in the field of applied machine learning

Shahebaz Mohammad

Senior Applied ML Engineer

Mayank Barnwal is a Senior Scientist at Tata Consultancy Services and an Adjunct Professor at IIT Bombay, specializing in control theory, machine learning, and optimization. With a Ph.D. from the University of Illinois at Urbana-Champaign, his research spans robust control algorithms, deep learning applications, and combinatorial optimization.

Mayank Barnwal

Senior Scientist

Kartik Nighania, an MLOps Engineer at Typewise, brings over seven years of AI experience across computer vision, NLP, and DevOps. Formerly Head of Engineering at Pibit.ai, he led AI-driven automation and infrastructure scaling. His expertise in CI/CD pipelines was honed at HSBC Technology, and his academic work includes AI publications and projects like ML-driven crop health detection

Kartik Nighania

MLOps Engineer

Lucas Soares is an AI Engineer at Otovo, focusing on AI-driven solutions through large language models (LLMs) and computer vision. With 6+ years of experience across sectors like biometrics and retail, he excels in developing machine learning tools

Lucas Soares

AI Engineer

Maarten Grootendorst is a Senior Clinical Data Scientist at IKNL (Netherlands Comprehensive Cancer Organization). He holds three master’s degrees in organizational psychology, clinical psychology, and data science, which he leverages to communicate complex machine-learning concepts to a wide audience

Maarten Grootendorst

Senior Clinical Data Scientist

Instructor-Led Live Workshops

Live workshops, real-world problems, expert insights (For a limited period!)

Learning Objective:

Supervised & Unsupervised ML Algorithms, with use cases
Classification & Regression Metrics
Techniques of preventing models from overfitting
Model Selection and comparison

Machine Learning for Generative AI

Learn Machine Learning for Generative AI

Learning Objective:

NLU Topics (POS Tagging, NER tags, noun chunking)
NLP Basics
End-to-end Sentiment Analysis project using ML algorithms and ANN
Advanced Sequence learning neural

NLP using Deep Learning

Implement NLP using Deep Learning

Learning Objective:

Fundamentals of Transformer Architecture
Adanced Model Architectures
Techniques for reducing model size
Applications in translation, summarization, and content generation

Advancing from Transformers to LLMs

Learn fundamentals of transformer architechture

Learning Objective:

Basics of Prompt Engineering & Understanding the role of prompts in AI interactions
Types of prompts, techniques for crafting effective prompts
Designing prompts for specific applications like translation, summarization, and content creation
Tailored prompts for tasks like question answering and data extraction

Prompt Engineering

Learn prompt engineering

Learning Objective:

Intro to RAG
Different document loaders, tokenisations and chunking strategies in Langchain
Word embedding techniques & creating RAG-based LLM apps
Experimenting open-source and proprietary LLMs

Advanced RAG using Langchain

Implement RAG using langchain

Learning Objective:

Basics of Fine-tuning
Data Preparation for Fine-tuning
Hyperparameter tuning for optimal performance, PEFT, other optimization tricks
Dealing with overfitting during the fine-tuning process

Fine Tuning LLMs

Learn to finetune large language models

Certificates of completion

Get Certified in GenAI from Analytics Vidhya, Fractal, and Western State University, and share your achievement with the world

Our Advisors

Prof. Tom Yeh leads the Imagine AI Lab at the University of Colorado Boulder, focusing on AI, HCI, Education, Ethics, and Neuroscience. He is the author of the popular AI by Hand series, has over 150 publications, and has received numerous university awards.

Dr. Tom Yeh

Dr. Andrei Lopatenko, with over two decades of experience in the technology sector, has led pioneering research and development in artificial intelligence, machine learning, and natural language processing at prominent organizations including Google, Apple, Walmart, eBay, and Zillow, as well as at the startup Ozlo, which was subsequently acquired by Facebook. He earned his PhD in Computer Science from the University of Manchester.

Andrei Lopatenko

Dr. Kirk Borne is a prominent data scientist with 40+ years of experience, founder of Data Leadership Group LLC, and an AI thought leader, career data professional, and research astrophysicist who has contributed to NASA's space science programs.