GenAI Pinnacle Plus Program

Industry-Focused Learning : Master GenAI and Agentic AI

  • 1:1 Mentorship with Generative AI experts
  • Advanced Curriculum with 200+ Hours of Learning
  • Master 26+ GenAI Tools and Libraries

300+

Hours of Immersive Learning

100%

Placement Assistance

30+

Hours of Live Workshops Quarterly

75+

1:1 Expert-Led Mentorships

Mission: GenAI Success Offer
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Become a GenAI and Agentic AI Expert : Start Now

How does the GenAI Pinnacle Plus Program Help You?

300+ Hours of Immersive Learning

  • Full-spectrum GenAI and Agentic AI learning with 14 modules
  • Master cutting-edge GenAI and Agentic AI frameworks and tools.

50+ Industry-Aligned Projects

  • Acquire real-world experience through projects that connect theory with practice.
  • Diverse projects designed to transform knowledge into expertise.

1:1 Expert Mentorship

  • Get expert insights from seasoned professionals
  • Accelerate your learning with a personalized roadmap to success

300+ Hours of Immersive Learning

  • Full-spectrum GenAI and Agentic AI learning with 14 modules
  • Master cutting-edge GenAI and Agentic AI frameworks and tools.

50+ Industry-Aligned Projects

  • Acquire real-world experience through projects that connect theory with practice.
  • Diverse projects designed to transform knowledge into expertise.

1:1 Expert Mentorship

  • Get expert insights from seasoned professionals
  • Accelerate your learning with a personalized roadmap to success

Curriculum Statistics

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50+ Projects

Hands-on learning with industry-relevant challenges.

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300+ Hours

In-depth GenAI and Agentic AI learning to transform your career

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40+ Libraries

Develop expertise in 40+ essential industry tools, libraries and frameworks.

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30+ Assignments

To turn knowledge into action

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75+ Mentorship Sessions

1:1 live mentorship session from GenAI and Agentic AI experts

Personalized Roadmap

Your ambition + our expertise = your custom path to mastery

  • 1Foundations for Generative AI
  • 2ML Foundations for Generative AI
  • 3DL Foundations for Generative AI
  • 4Build Applications using LLMs
  • 5Build RAG-Based Applications
  • 6Foundations for AI Agents
  • 7Getting Started with AI Agents using LangGraph, AutoGen & CrewAI
  • 8Build AI Agents using LangGraph, AutoGen & CrewAI
  • 9Real-World Projects on AI Agents
  • 10Finetune LLM-based applications
  • 11Deploy GenAI-Based Applications
  • 12Work with Diffusion Models
  • 13Decision-Making Essentials
  • 14Generative AI for Leaders
Personalized Roadmap Image
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Curriculum

From Python foundations to GenAI and Agentic AI : Master 14 cutting-edge modules

50+ Projects
300+ Hours
40+ Tools
30+ Assignments

  1. Introduction to Generative AI

  2. Essentials of Prompt Engineering

  3. Fine-Tuning RAGs and Agents

  1. Introduction to Responsible AI in the Generative AI Era

  1. Introduction to Python

  2. Working with Files and Databases

  3. Working with APIs

  4. Working with LLMs

  1. Build your first predictive model

  2. Preparing the dataset for Machine Learning Model

  3. Introduction to KNN algorithm

  4. Building your first KNN Model

  5. Evaluation Metrics

  1. Regression I

  2. Regression Il

  3. Project

  4. Decision Tree

  5. Regularization

  1. Introduction to Deep Learning

  2. Understanding the working of Neural Networks

  3. Improving Deep Neural Networks

  4. Guided Project

  1. Introduction to NLP

  2. Building a basic classification model

  3. NLP: Recurrent Neural Network

  4. Attention Mechanism and transformers

  5. Preparing for LLMs

  1. Introduction to Computer Vision

  2. Building Blocks for Image Recognition

  1. Introduction

  2. The Evolution of NLP

  3. What are Large Language Models?

  4. The Current State of the Art in LLMs

  5. Generative AI - Glossary

  1. Introduction to the LangChain Ecosystem

  2. Essentials of LangChain Expression Language (LCEL)

  3. Handling LLM Inputs and Outputs

  4. Projects on Prompt Engineering and Advanced LLM Chains

  5. Building LLM Chains and Conversational Applications

  1. Introduction to Prompt Engineering

  2. Core and Advanced Prompt Engineering Patterns

  3. Guidelines and Best Practices for Prompt Design

  4. Working with Commercial & Open-Source LLM APIs

  5. Hands-on Projects with Prompt Engineering and LLMs

  1. Introduction to Retrieval-Augmented Generation (RAG) Systems

  2. Building Retrieval Systems: Data Loading, Splitting & Chunking

  3. Implementing Vector Databases and Retrievers

  4. Projects: Document Retrieval & Advanced RAG Systems

  5. Building and Evaluating Complete RAG Pipelines

  1. Introduction to RAG Systems and LlamaIndex

  2. Core Components and Setup of LlamaIndex

  3. Customization and Advanced Techniques in LlamaIndex

  4. Evaluating RAG System Performance

  5. Building Powerful, Production-Ready RAG Solutions

  1. Introduction to Generative AI applications

  2. No-code Generative AI app Development

  3. Code-focused Generative AI App Development

  1. Coding with Windsurf

  1. Introduction to Agents

  2. Building Agents

  3. Working with Complex Agents

  1. Introduction to AI Agents and Agentic design

  2. Agentic AI Reflection Pattern

  3. Tool Use Pattern

  4. Agentic AI Planning Pattern

  5. Multi-Agent Pattern

  1. Introduction to AI Agents and Their Capabilities

  2. Building Reflection, Tool-Using, and Planning Agents

  3. Creating Multi-Agent Systems from Scratch

  4. Hands-on Project: Real-World AI Agent Development

  5. End-to-End Agentic Workflow with Practical Implementation

  1. Introduction to Tools and Tool Calling

  2. Essentials of AI Agents with LangChain

  3. Memory and Conversational Agents

  4. Project: Build a Text2SQL AI Agent

  5. Project: Build a Financial Analyst AI Agent

  1. Introduction to LangGraph

  2. Build AI Agents with LangGraph

  1. Introduction to CrewAI

  2. Core Components of CrewAI

  3. What sets CrewAI apart?

  1. Introduction to Tools and Tool Calling

  2. Essentials of AI Agents with LangChain

  3. Memory and Conversational Agents

  4. Project: Build a Text2SQL AI Agent

  5. Project: Build a Financial Analyst AI Agent

  1. Introduction to AutoGen

  2. Conversation Agents - Part 1

  3. Conversation Agents - Part 2

  4. Additional Applications with AG2

  5. Introduction to AutoGen Studio and Its Interface

  1. Course Introduction and Recap

  2. Advanced Components of crewAI

  3. Building Advanced Agents

  4. Assembling Complex Crew

  5. Optimizating Agents

  1. Introduction to Agentic RAG and LangGraph

  2. Popular Agentic RAG Architectures

  3. Project: Build a Router RAG System

  4. Project: Build an Agentic Corrective RAG System

  5. Project: Build an Adaptive RAG System

  1. Introduction to Autogen and AI Agents

  2. Setting up Chroma DB

  3. Setting up Autogen Agents

  4. Adding Search to Agents

  5. Testing/Evals

  1. Introduction to AI Agents and Multi-Agent Systems

  2. Agent-Based Hotel Reservation System

  3. Advanced AI Agent Orchestration

  4. Deploying and Scaling AI Agents

  1. Introduction to the Course

  2. Introduction to Finetuning LLMs

  3. Instruction Finetuning in Practice

  4. Parameter-Efficient Finetuning (PEFT)

  5. Prompt Learning PEFT Techniques

  1. Introduction to Training Large Language Models (LLMs) from Scratch

  2. Key Concepts and Workflow for LLM Training

  3. Step-by-Step Guide to Building Your Own LLM

  4. Aligning LLMs with Human Preferences

  5. Next Steps and Real-World Applications

  1. Introduction to Reinforcement Learning and Markov Decision Processes

  2. Core Methods: Dynamic Programming, Monte Carlo & Temporal Difference

  3. Deep RL Algorithms: PPO, DDPG, and Model-Free Control

  4. RLHF & DPO: Concepts, Techniques, and Algorithms

  5. Hands-on Implementation and Practical Applications

  1. Setting Context for the Course

  2. Kick-Start Your MLOps Journey

  3. Overview of Level 1 MLOps

  4. Overview of Level 2 MLOps

  5. MLOps Applications and Best Practices

  1. Introduction to AI Agent Operations

  2. Building an Agentic AI System

  3. Build an API for your Agentic AI System

  4. Deploying your AI Agent

  5. Testing and Monitoring your AI Agent

  1. UPCOMING

  1. Introduction and Overview of the Stable Diffusion Process

  2. Core Components and Architecture of Stable Diffusion

  3. Understanding Variational Autoencoders (VAEs)

  4. Deep Dive into Stable Diffusion Concepts and Workflows

  5. Hands-on Implementation: Building DDPM from Scratch

  1. Understanding Dalle 2

  2. Steps involved in training stable diffusion

  3. Mastering stability.ai and its tools

  4. Prompt Engineering Concepts for Stable Diffusion

  1. Img2img

  2. CIVITAI

  3. MidJourney

  4. InstructPix2Pix Paper review and ControlNet

  1. Why Decision Making is Hard

  2. Data in Decision Making

  3. Group Decision Making - Perceptions, Prejudices and Biases

  4. Group Decision Making - Role of Context, Hierarchy and Emotional Dynamics

  1. Introduction to Structured Thinking and Problem Definition

  2. Developing Clear Problem Statements (Parts 1 & 2)

  3. Problem-Solving Frameworks and Solution Finalization

  4. Pre-Solution Validation Checks

  5. Applying Human-Centered Design Principles

  1. Understanding Human Centered Design and Role of Empathy

  2. Discovery through Research Phase

  3. Insights through Synthesis Phase

  1. Why do Consultants need Generative AI?

  2. Generative AI in Practice

  3. A Consultants' Guide to Generative AI Tools

  1. Course Introduction

  2. Generative AI - The New Electricity

  3. Enterprising Generative AI

  4. Drive to succeed

  5. All you need to know

  1. Course Introduction and Defining AI Success

  2. Integrating AI with Engineering and Design

  3. Common Errors and Challenges in AI

  4. Building Organizational Effectiveness for AI Initiatives

  5. Strategies for Successful AI Implementation

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Libraries & Frameworks

Master 40+ GenAI and Agentic AI tools, libraries and frameworks for skill-building

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Build Your Portfolio with 50+ Industry-Relevant Projects

Accelerate your industry readiness with projects designed to tackle real-world challenges.

Learning Objective:

  • Train and evaluate LLMs from scratch
  • Learn LLM best practices and setup
  • Implement advanced computing strategies
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Diffusion Model Refinement

Finetune Diffusion models using ControlNets and InstructPix2Pix models

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Real Experience, Real Insights: Your Expert Mentors

Tap into decades of combined industry experience

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Chi Wang, Senior Staff Research Scientist at Google DeepMind, specializes in AI, machine learning, and data mining. Formerly a Principal Researcher at Microsoft Research, he holds a Ph.D. from UIUC and has driven advancements in web entity disambiguation, social network analysis, and AI-driven optimization.

Chi Wang

Senior Staff Research Scientist

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Miguel Otero Pedrido, a leader in AI and Machine Learning, is the Founder of The Neural Maze and a Senior Machine Learning Engineer at Dressipi. With 9 years of expertise in AI systems, MLOps, and AI agents, he has driven AI innovations at BBVA, Telefónica Tech, and Enagás.

Miguel Otero Pedrido

Founder

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Eleni Verteouri, GenAI Tech Lead and Director - Conversational Banking at UBS, drives AI innovation and strategic partnerships. With 12 years of expertise in Generative AI, product management, and risk modeling, she has led AI transformations in finance.

Eleni

GenAI Tech Lead and Director - Conversational Banking

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Qingyun Wu, founder of AG2 (formerly AutoGen) and Assistant Professor at Penn State University, brings over seven years of expertise in AI and machine learning. Her work spans AI agents, reinforcement learning, and algorithm optimization, with roles at Microsoft, Adobe, and Yahoo driving advancements in AI technologies.

Qingyun Wu

Creator and Founder

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Alessandro Romano, Senior Data Scientist at Kuehne+Nagel, has over six years of experience in AI and data science. With roles at FREE NOW and Cargonexx GmbH, he specializes in building AI-driven solutions and AI agents to automate workflows and enhance efficiency. A skilled public speaker, Alessandro effectively bridges technical concepts with diverse audiences.

Alessandro Romano

Senior Data Scientist

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Pio Scelina is an experienced AI Agent developer with over 6 years of expertise in creating advanced AI solutions. His strong research background keeps him at the leading edge of new tools and applications, constantly driving innovation. Known for his passion for exploring emerging technologies and enhancing AI-driven experiences, Pio has established a reputation as a trailblazer in AI-powered transformation.

Pio Scelina

AI Agent Developer

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Kamil Ruczynski is a seasoned AI Agent developer with over 7 years of experience in cutting-edge AI organisations. His deep expertise in research and development enables him to stay ahead of emerging trends and technologies, driving continuous innovation. Renowned for his dedication to pushing the boundaries of AI-driven applications, Kamil has earned recognition as a pioneer in creating transformative AI experiences

Kamil Ruczynski

AI Agent Developer

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Prashant Sahu, an IIT Bombay alumnus and seasoned Corporate Trainer in AI & ML, has over 17 years of diverse experience in areas like research, automation, and cryptography. His expertise extends to developing comprehensive Data Science training materials, including curriculum, case studies, and projects.

Prashant Sahu

Manager - Data Science - Instructor

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Apoorv Vishnoi, a seasoned professional with over 13 years of experience, including more than 10 years in Machine Learning and AI. He holds an MBA from the prestigious Indian School of Business and several certifications in Data Science and Deep Learning. His ability to simplify complex concepts in Data Science and Machine Learning has established him as a respected and influential instructor.

Apoorv Vishnoi

Head - Training Initiative

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Instructor-Led Live Workshops

Live GenAI and Agentic AI workshops : Solve real-world problems with expert insights

Learning Objective:

  • Differentiate RAG from prompt engineering and fine-tuning.
  • Learn when to use RAG versus other approaches.
  • Understand Retrieval-Augmented Fine-Tuning (RAFT).
  • Build, tune, and evaluate a RAG pipeline.
Mastering-RAG-Systems

Upcoming7:00 PM - 10:00 PM (IST) IST

Mastering RAG Systems I

Jyant Mahara
Zscaler

Learning Objective:

  • Learn LangChain fundamentals for building AI Agents.
  • Explore prompts, chat models, tools, and function calling.
  • Develop tool-use Agents with hands-on exercises.
  • Integrate memory to create adaptive conversational Agents.
Introduction-to-LangGraph-for-Building-AI-Agents

Upcoming7:00 PM - 10:00 PM (IST) IST

Introduction to LangGraph for Building AI Agents

Dipanjan Sarkar
Analytics Vidhya

Learning Objective:

  • Understand ML algorithms, Data Preparation, and Model Building.
  • Master Linear & Logistic Regression with error minimization and key metrics.
  • Learn data handling techniques like missing value treatment & encoding.
  • Apply regularization and interpret confusion matrices for classification.
Machine-Learning-basics

Upcoming7:00 PM - 10:00 PM (IST) IST

Machine Learning Basics

Arpit Jain
Mavenir

Learning Objective:

  • Explore Decision Trees, SVM, and KNN for classification and regression.
  • Learn to handle overfitting, multicollinearity, and unbalanced data.
  • Master model selection, cross-validation, and hyperparameter tuning.
  • Enhance model performance with ensemble methods like bagging and boosting.
machine-learning-advanced

Upcoming7:00 PM - 10:00 PM (IST) IST

Machine Learning Advanced

Arpit Jain
Mavenir

Learning Objective:

  • Understand Neural Networks, activation functions, and optimization techniques.
  • Build Artificial Neural Networks (ANNs) for structured data.
  • Learn backpropagation, gradient descent, and overfitting prevention.
  • Explore the basics of CNNs and RNNs for Deep Learning applications.
Deep-Learning-Using-Pytorch

Upcoming7:00 PM - 10:00 PM (IST) IST

Deep Learning Using Pytorch

Prashant Sahu

Learning Objective:

  • Learn vector space models like Bag of Words and TF-IDF.
  • Explore word embeddings with Word2Vec, GloVe, and FastText.
  • Understand embeddings at word, sentence, and document levels.
  • Build sequential models (RNN, LSTM, GRU) and apply them to NLP tasks.
NLP_using_Deep_Learning

Upcoming7:00 PM - 10:00 PM (IST) IST

NLP using Deep Learning

Prashant Sahu

Learning Objective:

  • Understand the importance of prompt engineering.
  • Learn to craft clear, specific, and contextual prompts.
  • Apply few-shot prompting techniques for better AI responses.
  • Refine generated content through iterative improvements.
Mastering-Prompt-Engineering

Upcoming7:00 PM - 10:00 PM (IST) IST

Mastering Prompt Engineering I

Jyant Mahara
Zscaler

Learning Objective:

  • Manage multi-turn conversations effectively.
  • Apply advanced prompting techniques for complex tasks.
  • Design chatbot scenarios and analytical reports.
  • Understand AI limitations and ethical considerations.
Mastering-Prompt-Engineering

Upcoming7:00 PM - 10:00 PM (IST) IST

Mastering Prompt Engineering II

Jyant Mahara
Zscaler

Learning Objective:

  • Compare GraphRAG with traditional RAG systems.
  • Build and store knowledge graphs in graph databases.
  • Create and evaluate GraphRAG pipelines.
  • Understand the architecture and key components of a RAG system.
Mastering-RAG-Systems

Upcoming7:00 PM - 10:00 PM (IST) IST

Mastering RAG Systems II

Jyant Mahara
Zscaler

Learning Objective:

  • Learn LangChain fundamentals for building AI Agents.
  • Explore prompts, chat models, tools, and function calling.
  • Develop tool-use Agents with hands-on exercises.
  • Integrate memory to create adaptive conversational Agents.
Building-AI-Agents-with-LangChain

Upcoming7:00 PM - 10:00 PM (IST) IST

Building AI Agents with LangChain

Dipanjan Sarkar
Analytics Vidhya

Learning Objective:

  • Learn memory management and snapshots for AI Agents.
  • Build conversational Agents with persistent memory.
  • Develop a financial analyst Agentic system.
  • Create adaptive Agents that retain context and improve over time.
Building-Advanced-AI-Agents-with-LangGraph-Conversational-Agents

Upcoming7:00 PM - 10:00 PM (IST) IST

Building Advanced AI Agents with LangGraph - I (Conversational Agents)

Dipanjan Sarkar
Analytics Vidhya

AV Assisted Placements

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Our Advisors

Our advisors ensure our programs are innovative, impactful, and industry-aligned.

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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

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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

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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.

Dr. Kirk Borne

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AV Learners Spotlight

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All my expectations from the course and workshops have been fulfilled

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I found future learning with Gen AI very interesting

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The program has all the cutting edge technologies covered

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I really value the personalised mentorship sessions which came with the program

I've had the pleasure of witnessing the exceptional talent nurtured by Analytics Vidhya's hackathons.

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Mentors of Analytics Vidhya have business experience and are practitioners themselves

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The content Analytics Vidhya provided was far better than any other organisation

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GenAI Pinnacle Plus Program comes with 7 days no questions asked Money Back Guarantee. If the Program is bought in pre-launch offer or on discounted price, then the fee paid is non-refundable. For more T&C, Click here

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The GenAI Pinnacle Plus Program sets itself apart by offering a unique combination of 1:1 mentorship, over 300 hours of advanced Generative AI and Agentic AI learning, and real-world project experience. This GenAI certification program focuses on hands-on learning using more than 40 Generative AI tools and frameworks, ensuring you stay ahead in the evolving AI industry.

The GenAI Pinnacle Plus Program stands out by offering comprehensive coverage of cutting-edge technology topics such as AI Agents and other emerging trends that are making headlines and are poised to dominate the market in the coming months. It ensures you are equipped with the most relevant and forward-thinking skills to excel in a rapidly evolving AI landscape.

This GenAI Pinnacle Plus Program is perfect for professionals, students, and AI enthusiasts who want to specialize in Generative AI, enhance their career prospects in GenAI and AI Agents, or remain at the forefront of technology.

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