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Gen AI

Master generative and agentic AI—from AI foundations and intelligent agents to transformers, LLMs, RAG, and autonomous agent systems. Industry-aligned curriculum from SKILLUMNI for the future of AI.

1,500+ Students
4.8
Beginner to Advanced

What You'll Learn

  • AI evolution, intelligent agents, search algorithms, and knowledge representation
  • Reasoning under uncertainty, decision making, and reinforcement learning
  • Neural networks, deep learning, transformers, and generative AI fundamentals
  • Large language models, GANs, VAEs, and diffusion models
  • Agentic AI, ReAct, chain-of-thought, and multi-agent systems
  • RAG, vector databases, autonomous AI systems, and alignment

About This Course

This Gen AI course covers artificial intelligence from foundational concepts to generative and agentic systems. You will learn the evolution of AI, intelligent agents, problem solving, search algorithms, knowledge representation, and reasoning under uncertainty.

Progress through reinforcement learning, neural networks, CNNs, RNNs, and transformers; then dive into generative AI, LLMs, GANs, VAEs, and diffusion models. The course concludes with agentic AI—tool usage, planning, memory, RAG, multi-agent collaboration, and autonomous AI system design.

Course Benefits

  • 35+ hrs of Training
  • Industry-based assessments
  • Outcome-based learning
  • Hands-on projects
  • Lifetime LMS Access
  • Industry certification

Course Curriculum

1. Overview of Artificial Intelligence (Generative + Agentic)

  • • Definition, Goals & Scope of AI
  • • Evolution: Symbolic → Statistical → Deep Learning → Generative → Agentic
  • • Branches of AI
  • • Narrow AI vs AGI vs Superintelligence
  • • AI Development Lifecycle
  • • AI in Industry & Future Trends

2. Intelligent Agents & Rational Decision Making

  • • Agent Definition & Environments
  • • Rational Agents & Utility
  • • Types of Environments
  • • PEAS Framework
  • • Agent Architectures Overview

3. Problem Solving & State Space Search

  • • Problem Formulation
  • • State Space Representation
  • • Tree vs Graph Search
  • • Complexity of Search

4. Search Algorithms

  • • Breadth First Search (BFS)
  • • Depth First Search (DFS)
  • • Uniform Cost Search
  • • Informed Search Concepts
  • • A* Algorithm

5. Knowledge Representation

  • • Propositional Logic
  • • First Order Logic
  • • Inference Systems
  • • Automated Reasoning

6. Knowledge-Based Systems

  • • Forward Chaining
  • • Backward Chaining
  • • Rule-Based Systems
  • • Knowledge Graphs

7. Reasoning Under Uncertainty

  • • Probability Theory in AI
  • • Bayesian Reasoning
  • • Conditional Independence

8. Decision Making in AI

  • • Utility Theory
  • • Decision Making Under Uncertainty
  • • Markov Decision Processes (Overview)

9. Reinforcement Learning Fundamentals

  • • RL Framework
  • • Exploration vs Exploitation
  • • Real-World Applications

10. Neural Networks

  • • Perceptron Algorithm
  • • Activation Functions
  • • Backpropagation (Conceptual)
  • • Gradient Descent

11. Deep Learning Architectures

  • • CNN Overview
  • • RNN & LSTM Overview
  • • Representation Learning
  • • Introduction to Transformers

12. Introduction to Generative AI

  • • Generative vs Discriminative Models
  • • Foundation Models
  • • Self-Supervised Learning

13. Transformer Architecture

  • • Pretraining Objectives
  • • Fine-Tuning Methods
  • • Alignment & RLHF
  • • Emergent Abilities

14. Large Language Models (LLMs)

  • • Pretraining Objectives
  • • Fine-Tuning Methods
  • • Alignment & RLHF
  • • Emergent Abilities

15. Advanced Generative Models

  • • GANs
  • • VAEs
  • • Diffusion Models
  • • Multimodal Models

16. Introduction to Agentic AI

  • • AI Agents vs LLMs
  • • Tool Usage
  • • Reasoning Loops
  • • Planning & Memory in Agents

17. Agent Architectures & Reasoning

  • • ReAct Framework
  • • Chain-of-Thought Reasoning
  • • Tree-of-Thought Reasoning

18. Agent Memory & Knowledge Systems

  • • Agent Memory Systems
  • • Vector Databases
  • • Retrieval-Augmented Generation (RAG)

19. Multi-Agent Systems

  • • Multi-Agent Collaboration
  • • Communication & Coordination

20. Autonomous AI Systems & Future

  • • Planner–Executor Architectures
  • • Long-Horizon Reasoning Challenges
  • • Alignment Challenges
  • • Evaluation of LLM Systems
  • • Scaling & Optimization of AI Systems

Requirements

  • Basic programming or technical aptitude; interest in AI and automation
  • No prior analytics experience required; beginners welcome
  • Computer with internet connection
  • Willingness to learn AI concepts, models, and agent-based systems

Material Includes

  • 35+ Hours of Video Lectures
  • Lifetime LMS Access
  • Section Quizzes & Assessments
  • Industry-Based Hands-on Projects
  • Certificate of Completion

Why Choose This Course

Industry-Aligned Curriculum

Structured curriculum from AI foundations to generative models, LLMs, and agentic systems

Hands-On Projects

Build agent workflows, apply LLMs, and design RAG-powered AI solutions

Industry Certification

Earn a recognized certificate for roles in generative and agentic AI, BI, and data science

Mentor Support

Get guidance from experts in generative and agentic AI and machine learning

Flexible Learning

Learn with lifetime access to course materials, projects, and updates

Career Support

Access placement assistance and connect with professionals in AI and ML engineering

Ready to Master Gen AI?

Join students learning generative AI, LLMs, transformers, RAG, and agentic systems. Start your Gen AI journey today.

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