Generative AI  /  Beginner to Mastery

Mastering in AI and Generative AI

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Course Duration

300 Hours

 

Course Material

Live. Online. Interactive.

Enhance skills and open doors to advanced AI roles.

Master generative AI, GANs, and RAG methods.

Connect with peers and industry leaders.

Earn a recognized certification that boosts career prospects.

KEY HIGHLIGHTS OF MASTERING IN AI AND GENERATIVE AI PROGRAM

1) Weekly sessions with industry professionals

2) Dedicated Learning Management Team

3) 300 hours of hands-on learning experience

4) Over 100 hours live sessions spread across 11 months

5) 100 hours of self-paced Learning

6) Learn from IIT Faculty & Industry experts

🔺More than 40+ industry-related projects and case studies

🔺24*7 Support

🔺1:1 Mock Interview

🔺Designed for both working professionals and fresh graduates

🔺Competitive Edge and Innovation

🔺 Personalised mentorship sessions with industry experts

🔺Dedicated Learning Management Team

🔺No-Cost EMI Option

🔺High Demand and Career Opportunities

🔺Problem-Solving and Critical Thinking

WHY JOIN MASTERING IN AI AND GENERATIVE AI PROGRAM?

Hands-On Learning

Gain practical experience with advanced tools like GPT, DALL-E 2, and Hugging Face Transformers.

Comprehensive Skill Set

Master everything from Python programming to cutting-edge AI techniques.

Stay Ahead

Learn the latest AI and generative technologies shaping the future.

Career Advancement

Boost your qualifications and open doors to advanced AI roles.

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Mastering in AI and Generative AI OVERVIEW

This Program offers a deep dive into fundamental AI technologies and essential libraries for machine learning, image processing, etc. Participants will learn cutting-edge tools such as Hugging Face Transformers, GPT, DALL-E 2.0, MidJourney, GANs, RAG, and LanguageChain. The curriculum integrates theoretical understanding with real-world use cases to develop a skills-rich ecosystem capable of utilizing AI and generative technologies at an advanced level.

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Mastering in AI and Generative AI Objectives

The course aims to provide participants with a thorough understanding of AI technologies with its more high-level programming side. After completing this, the student will be proficient in Python programming and have experience working with Hugging Face Transformers, GPT, DALL-E 2, and MidJourney. Then they can integrate those levels of solutions into their AI application. And will be equipped with hands-on experience in GANs, RAG, and LangChain to face the complex challenges & innovations in the AI community.

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Why Learn Mastering in AI and Generative AI ?

Build Predictive Models

Learn machine learning and deep learning techniques to create intelligent systems for accurate predictions.

Unlock Data Insights

Discover hidden patterns through data analysis and feature engineering to drive informed decision-making.

Explore Advanced AI

Dive into deep learning, NLP, and reinforcement learning to design solutions for complex tasks.

Explore Advanced AI

Dive into deep learning, NLP, and reinforcement learning to design solutions for complex tasks.

Hands-On with Generative AI

Develop skills with tools like DALL-E 2, MidJourney, and GANs for creative image generation and data synthesis.

Innovate with LLMs & LangChain

Leverage Large Language Models, GPT, and LangChain to create and manage cutting-edge AI applications.

Enhance Communication

Effectively convey complex information and findings using data visualization techniques.

Drive Innovation

Contribute to the development of cutting-edge technologies and solutions through a deep understanding of these tools.

Program Advantages

✅ Industry-relevant skills in high-demand AI technologies like GPT, DALL-E 2, and Hugging Face Transformers.

✅ Comprehensive curriculum covering Python programming to advanced AI techniques such as GANs and RAG.

✅ Hands-on experience through real-world projects and applications.

✅ Expert guidance from experienced instructors in AI and generative technologies.

✅ Enhanced career opportunities with skills valued in the tech industry.

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Mastering in AI and Generative AI program Certifications

Mastering in AI and Generative AI Curriculum

Module 01 - Machine Learning
Lecture 01: Introduction to ML
Lecture 02: Regression Techniques
Lecture 03: Error Metrics
Lecture 04: Classification Report and AUC ROC
Lecture 05: Unsupervised Learning Techniques
Module 02 - Deep Learning
Lecture 06: Introduction to Neural Networks
Lecture 07: Activation Functions and Optimizers
Lecture 08: Gradient Descent Methods
Lecture 09: Time Series Analysis
Lecture 10: ARMA and ARIMA
Module 3 - Computer Vision
Lecture 11: Introduction to Image Processing
Lecture 12: Convolution, Padding, Pooling
Lecture 13: Forward & Backward Propagation for CNN
Lecture 14: CNN Architectures
Module 04 - Deep Learning
Lecture 15: Introduction to Text Mining
Lecture 16: Sentiment Analysis & Topic Modeling
Lecture 17: BERT and Text Classification
Lecture 18: Automatic Speech Recognition & Web Scraping
Module 5 - Reinforcement Learning(RL)
Lecture 19: RL Framework
Lecture 20: Types of RL Systems
Module 06 - Foundations of Generative AI
Lecture 21: Evolution of AI (Rule-based → ML → GenAI → Agentic AI), Hype vs Reality, Industry Adoption of GenAI, Ethical & Responsible AI
Lecture 22: How Generative AI Works: LLM intuition, Tokens, Embeddings, Context Window, Capabilities & Limitations (Hallucination, Bias, Cost)
Lecture 23: Multimodal AI Systems: Text, Image, Tables, Documents. Industry Applications. Case Study: Invoice and financial report understanding
Lecture 24: Core Generative AI Tasks: Text generation, Classification, Summarization, Question Answering. Hands-on Case Study: Resume screening and document summarization
Lecture 25: Prompt Engineering Fundamentals: Zero-shot, Few-shot, Role Prompting, Prompt Templates. Case Study: Marketing content generation
Module 07 - Prompt Engineering, RAG & Multimodal RAG
Lecture 26: Advanced Prompting: Prompt Debugging, Guardrails, Prompt Evaluation, Response Optimization. Hands-on Case Study: Improving incorrect chatbot responses
Lecture 27: Retrieval-Augmented Generation (RAG): Embeddings, Vector Search, RAG Architecture. Hands-on Case Study: Chat with company policy documents
Lecture 28: Multimodal RAG: Text + Image + Table Retrieval, Document Intelligence. Case Study: Invoice and scanned document Q&A system
Module 08 - Agentic AI Systems & Framework Internals
Lecture 29: Introduction to Agentic AI: Agent vs Chatbot Workflow, Agent Lifecycle, Levels of Autonomy, Human-in-the-loop Systems. Case Study: AI Research Assistant
Lecture 30: Agent Architecture & Design Patterns: Planner-Executor-Evaluator, ReAct Pattern, Tool-Use Pattern, Reflection. Hands-on Case Study: Recruiter Agent design
Lecture 31: Agent Memory, Tools & Planning: Short-term vs Long-term Memory, Tool Calling, Feedback Loops. Case Study: Customer support agent with memory
Lecture 32: Agent Framework Internals (Conceptual): How frameworks manage chains, agents, tools and memory. Positioning of LangChain. Design considerations without deep syntax
Module 09 - Fine-Tuning, No-Code Agents & Capstone Project
Lecture 33: LLM Fine-Tuning from an Industry Perspective: Prompting vs RAG vs Fine-Tuning, PEFT and LoRA concepts, Cost, Risk and Governance Considerations
Lecture 34: No-Code and Low-Code Agentic AI: Use cases, Benefits and Limitations, Visual Agent Design. Demo: No-code content or support agent
Lecture 35: Capstone Design Session: End-to-End Generative AI and Agentic AI Solution. Hands-on Project: AI Customer Support Supervisor Agent

Mastering in AI and Generative AI Skills Covered

Mastering in AI and Generative AI Tools Covered

Mastering in AI and Generative AI Program Benefits

Cutting-Edge Knowledge
Stay ahead with the latest advancements in AI and generative technologies.
Hands-On Experience
Apply your learning in real-world scenarios using state-of-the-art tools.
Comprehensive Learning
Gain a holistic understanding of AI, from foundational concepts to advanced applications
Career Growth
Enhance your employability in a rapidly evolving and high-demand field.
Expert Support
Learn from industry professionals with deep expertise in AI and machine learning.
Networking Opportunities
Connect with peers and professionals, expanding your professional network.
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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.