AIGENERATIVE · AGENTIC

Course details

Generative AI + Agentic AI

From AI foundations to LLMs and agentic systems you can ship.

IntermediateLevel
38 Hours+Hours
3 MonthsDuration
390+Students

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Taught by Tanvi Agarwal · GenAI / Chatbot Mentor

CertificateIncluded on completion

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Project-based learning

Build real work you can show in interviews.

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Lifetime access

Revisit lessons anytime after you enroll.

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Mentor support

Learn with guidance from industry experts.

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Career-ready skills

Practical modules aligned to real roles.

Overview

What this course covers

Program Curriculum 2026 Edition — 3-month professional program (12 weeks, 2–3 classes/week). Path: AI Foundations → Intelligent Systems → Machine Intelligence → Generative AI → LLMs → Agentic AI. Instructor-led labs plus an industry capstone.

Outcome

Finish with practical skills and a portfolio-ready project in Generative AI + Agentic AI you can use for jobs, freelancing, or your next role.

Skills

What you'll learn

AI foundations for modern builders
Generative AI systems & prompting
Large language models in practice
Agentic AI workflows
Ship assistants and automation agents
Roles: AI Engineer, GenAI Engineer, LLM Developer, Agentic AI Developer

Audience

Who this course is for

Beginners

Starting Generative AI + Agentic AI from scratch and want a clear path.

Students & grads

Building a portfolio and interview-ready skills.

Working pros

Upskilling into ai roles with practical projects.

Before you start

Prerequisites

Basic computer and internet skills
Curiosity and consistency to practise weekly
Open to learners of every background

Syllabus

Curriculum overview

12 modules · week-by-week topics · labs · certificate on completion

01Week 1: Overview of Artificial Intelligence

Phase 01 · AI Foundations — see the full AI landscape before you build.

Topics covered

Definition, goals & scope of AI
Evolution: Symbolic → Statistical → Deep Learning → Generative → Agentic
Branches of AI
Narrow AI vs general intelligence
AI development lifecycle
Industry applications & future trends

Lab: AI ecosystem mapping exercise

02Week 2: Intelligent Agents & Rational Decision Making

Learn how agents sense, decide, and act in an environment.

Topics covered

Agent definition & environments
Rational agents
Utility-based decisions
Types of AI environments
PEAS framework
Agent architectures

Lab: Design a PEAS-based intelligent agent

03Week 3: Problem Solving & State Space Search

Solve problems by searching systematically through possibilities.

Topics covered

Problem formulation
State space representation
Tree search vs graph search
BFS, DFS, UCS
Depth-limited & iterative deepening

Lab: Implement and compare search algorithms

04Week 4: Informed & Adversarial Search

Use informed search and game-tree decisions.

Topics covered

Heuristics & evaluation functions
Greedy best-first & A*
Heuristic design
Minimax & alpha-beta pruning
Multi-agent environments

Lab: Build A* pathfinding + basic Minimax agent

05Week 5: Knowledge Representation & Automated Reasoning

Represent knowledge so systems can reason about it.

Topics covered

Propositional & first-order logic
Inference systems
Forward / backward chaining
Resolution
Rule-based systems & knowledge graphs

Lab: Build a rule-based reasoning system

06Week 6: Planning & Reasoning Under Uncertainty

Plan actions and decide when the world is uncertain.

Topics covered

Classical planning & STRIPS
Planning vs search
Hierarchical planning
Probability in AI
Bayesian reasoning

Lab: Create a basic AI planning & decision model

07Week 7: Probabilistic Models & Decision Systems

Phase 02 · Generative & Agentic Intelligence — decisions under uncertainty.

Topics covered

Bayesian & Markov networks
Inference in graphical models
Utility theory
Markov decision processes
Policies & value functions

Lab: Design a probabilistic decision workflow

08Week 8: Reinforcement Learning & Neural Networks

Connect learning from rewards with neural network basics.

Topics covered

RL framework & Bellman equations
Value / policy iteration
Exploration vs exploitation
Perceptron, activations, backpropagation
Gradient descent

Lab: Basic neural network + RL decision exercise

09Week 9: Deep Learning & Generative AI Foundations

Move from classical ML into modern generative systems.

Topics covered

CNN / RNN / LSTM overview
Transformers introduction
Representation learning
Generative vs discriminative models
Foundation models & self-supervised learning

Lab: Architecture exploration & GenAI model comparison

10Week 10: Transformers & Large Language Models

Work with LLMs the way product teams do.

Topics covered

Attention & multi-head attention
Encoder-decoder architecture
Scaling laws & pretraining
Fine-tuning, alignment & RLHF
Emergent abilities

Lab: LLM API integration + prompt engineering lab

11Week 11: Advanced Generative AI & Agentic AI

Extend generation into agents that use tools and memory.

Topics covered

GANs, VAEs, diffusion models
Multimodal models
AI agents vs LLMs
Tool usage & reasoning loops
Planning & agent memory

Lab: Build a tool-using AI agent

12Week 12: Agent Architectures & Autonomous AI Systems

Ship an autonomous agent system and present the capstone.

Topics covered

ReAct & tree-based reasoning
Multi-agent systems
Planner-executor architecture
Vector databases & long-horizon tasks
Evaluation, scaling & optimisation

Lab: Build & present an autonomous AI agent (capstone)

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After the course

You'll walk away ready

By completing this course, you will have the practical skills and a portfolio project in Generative AI + Agentic AI to confidently move forward in your career.