Explore generative ai & agentic ai through a 33-module curriculum and 4 project topics. Build foundations in Python, NLP, machine learning and transformers, then explore LLM applications, prompts, retrieval-augmented generation, AI agents and multi-agent workflows. The curriculum includes memory, MCP, human approval, evaluation, guardrails and deployment.
Online India & worldwideClassroom Learn at a branchHybrid Online + classroom
Classroom: Dilsukhnagar, Hyderabad
Online: USA, Canada, UK, Australia, New Zealand and Germany
Ask about your preferred mode, branch and upcoming batch.
Learn Generative AI & Agentic AI through practical study.
Build foundations in Python, NLP, machine learning and transformers, then explore LLM applications, prompts, retrieval-augmented generation, AI agents and multi-agent workflows. The curriculum includes memory, MCP, human approval, evaluation, guardrails and deployment.
Your willingness to learn comes first
Who can join?
Build new skills or upgrade your technology knowledge. LearnAI welcomes students pursuing a degree, graduates, working professionals and learners from relevant domains.
Basic computer knowledge
Basic computer knowledge is recommended to help you participate in practical learning.
Students and professionals
Join while pursuing a degree, after graduation, or to develop skills for your relevant domain.
No prior technology knowledge required
You can begin without previous knowledge of the course technology. Bring your interest and willingness to learn.
No age limit
Learners of all ages are welcome. Learners under 18 should enrol with a parent or guardian’s consent.
What the curriculum develops
Skills you will practise.
Develop Python and AI foundations
Study data preparation, NLP, machine learning and transformers.
Build LLM applications
Connect prompts, APIs, structured outputs and user interfaces.
Retrieve useful knowledge
Explore embeddings, vector databases, hybrid search and RAG.
Develop tool-using agents
Study function calling, memory, state and MCP.
Design agentic workflows
Explore multi-agent patterns, human approval and recovery.
Evaluate and deploy systems
Study guardrails, tracing, cost, latency and LLMOps.
The complete learning path
Generative AI & Agentic AI course curriculum.
Explore all 33 modules. Open a module to view its topics, and discuss the delivery plan for your batch.
01 / Python and AI foundations
MODULE 01Python Fundamentals
Python Introduction
Variables & Data Types
Conditions & Loops
Functions & Collections
File Handling
Exceptions & OOP
Modules & Packages
Virtual Environments
MODULE 02Python Libraries for AI
NumPy Arrays
Pandas DataFrames
Data Manipulation
Matplotlib Plots
Seaborn Basics
CSV / Excel / JSON
API Requests
Git & GitHub Basics
MODULE 03Statistics for AI
Descriptive Statistics
Probability Basics
Distributions
Variance & Deviation
Sampling
Correlation
Outliers
Scaling Concepts
MODULE 04Numerical Data Prep
Data Investigation
Missing Values
Duplicate Handling
Outlier Analysis
Cleaning & Wrangling
Transformation
Feature Engineering
Preprocessing
MODULE 05Text Data & NLP
Text Investigation
Text Cleaning
Normalization
Tokenization
Stop Words
Stemming / Lemmas
TF-IDF & N-grams
Sentence Embeddings
MODULE 06Machine Learning
ML Workflow
Supervised Learning
Unsupervised Learning
Regression
Classification
Clustering
Train / Test Split
Model Evaluation
MODULE 07Deep Learning
Neural Networks
Neurons & Layers
Activation & Loss
Forward Propagation
Backpropagation
Optimizers
Embeddings
CNN / RNN / LSTM
MODULE 08Transformers
Tokens & Embeddings
Positional Encoding
Self-Attention
Query / Key / Value
Multi-Head Attention
Feed-Forward Layers
Encoder & Decoder
BERT vs GPT
02 / Generative AI and LLM systems
MODULE 09GenAI Fundamentals
Traditional AI vs GenAI
Foundation Models
Text Generation
Image & Audio Generation
Video & Multimodal AI
Use Cases & Limitations
Hallucinations
Responsible Use
MODULE 10Large Language Models
LLM Architecture
Tokens & Context Window
Pretraining
Instruction Tuning
Fine-Tuning Concepts
Inference Settings
Temperature & Top-p
Open vs Proprietary
MODULE 11LLM Models & Platforms
OpenAI
Anthropic Claude
Google Gemini
NVIDIA Models
Hugging Face
Ollama & Local LLMs
Model Selection
Provider Limitations
MODULE 12Prompt Engineering
System / User Instructions
Zero-shot & Few-shot
Role & Context Prompting
Prompt Templates
Prompt Chaining
Structured Prompting
JSON Outputs
Prompt Evaluation
Injection Awareness
MODULE 13LLM APIs & Integration
API / REST Fundamentals
HTTP & JSON
API Keys & .env
Provider SDKs
Model Parameters
Streaming Outputs
Errors & Retries
Rate Limits
Usage & Costs
MODULE 14AI App Development
Python LLM Integration
FastAPI Endpoints
Request / Response
Input Validation
Structured Outputs
UI Integration
Session Handling
Logging & Testing
Deployable Application
03 / Retrieval-augmented generation
MODULE 15Embeddings & Vector Databases
Word / Sentence Embeddings
Semantic Similarity
Cosine Similarity
Vector Search
Indexing & Metadata
ChromaDB / FAISS
Vector Store Updates
Embedding Evaluation
MODULE 16RAG Fundamentals
Why RAG?
RAG Architecture
Knowledge Base
Document Loading
Parsing & Chunking
Embedding Pipeline
Retrieve / Generate
Grounded Answers & Sources
MODULE 17Retrieval Techniques
Keyword & Full-text
Sparse / Dense Search
Semantic Search
Metadata Filtering
Hybrid Search
Similarity / MMR
Multi-Query Retrieval
Reranking
MODULE 18Advanced RAG & Agentic RAG
Chunking Strategies
Query Expansion & Rewriting
Parent-child Retrieval
Conversational RAG
Hybrid Search + Reranker
Multimodal RAG Concepts
Corrective & Agentic RAG
RAG Evaluation & Guardrails
MODULE 19Real-World RAG Project
Enterprise Knowledge Assistant
PDF / Document Ingestion
Chunking & Vector Index
Hybrid Retrieval + Reranking
LLM Answer + Citations
Conversation-aware Interface
Evaluation & Safe Fallback
Deployment
04 / AI agents and tool integration
MODULE 20AI Agent Fundamentals
Agent vs Chatbot / RAG
Goals & Instructions
Model + Tools
Reasoning / Planning
Action / Observation
Agent Decision Loop
ReAct Concepts
Agent Limitations
MODULE 21Tools & Function Calling
Tools vs Functions
Python Functions as Tools
API / Database Tools
Search & File Tools
Typed Arguments
Tool Result Handling
Retries & Errors
Permissions & Approvals
MODULE 22Agent Memory & State
Conversation Memory
Session / Working Memory
Short-term Memory
Long-term Memory
Vector-based Memory
State Management
Persistent Checkpoints
Memory Quality / Privacy
MODULE 23Model Context Protocol
MCP Introduction
Host / Client / Server
Tools & Resources
Prompts
Connecting Data Sources
Building MCP Servers
Agent MCP Integration
Secure Access Basics
MODULE 24Agent Frameworks
Framework-free Agent
LangChain Fundamentals
Tool Integration
LangGraph Nodes / Edges
Shared Graph State
Conditional Routing
Checkpoints & Retries
Traceable Workflows
MODULE 25AI Agent Project
Customer Support Agent
Knowledge-base RAG
Custom API Tools
Database Retrieval
Ticket Creation
Session Memory
Guardrails
Human Escalation
User Interface
05 / Agentic AI and multi-agent systems
MODULE 26Agentic AI Fundamentals
Agent vs Agentic System
Goals & Autonomy
Reasoning / Planning
Action / Feedback
Reflection
Replanning
Success Criteria
State & Control
MODULE 27Workflow Patterns
Sequential & Parallel
Routing / Conditions
Supervisor / Worker
Planner / Executor
Reviewer Pattern
Reflection Loop
Evaluator / Optimizer
Retries / Recovery
MODULE 28Multi-Agent Systems
Agent Roles
Specialized Agents
Task Delegation
Agent Communication
Shared Memory / State
Agent Handoffs
Supervisor Orchestration
Conflict Handling
MODULE 29Human Approval & HITL
Approval Gates
Manual Reviews
Sensitive Operations
Interrupt / Resume
Checkpoints
Escalation Rules
Human Feedback
Audit Trail
MODULE 30Guardrails & Security
Input / Output Checks
Tool Permissions
Prompt Injection
PII Protection
Privacy Basics
Safe Failure
Access Control
Human Escalation
MODULE 31Evaluation & Tracing
LLM Output Evals
RAG Quality Evals
Agent Task Completion
Tool Choice & Accuracy
Agent Trajectories
Faithfulness / Relevance
Cost & Latency
Logs and Traces
MODULE 32Deployment & LLMOps
FastAPI & UI
Docker Basics
Secrets & Auth
Deploying Endpoints
Monitoring
Token / Cost Tracking
Error Handling
Versioning / Tests
MODULE 33Agentic Capstone
Autonomous Platform
Supervisor + Workers
RAG + Tools
MCP Connections
Agent Shared State
Human Approval
Quality Evaluation
Deployment Demo
Explore the concepts visually
A visual look at the subject
Illustrative overviews. Refer to the curriculum for the topics included in your course.
Generative AI overview · Open image to enlargeGenerative AI tools · Open image to enlarge
Ask about the learning datasets, project selection, guidance and delivery for your batch.
GenAI Application
Connect prompts, LLM APIs and structured outputs.
Enterprise RAG
Combine documents, embeddings, hybrid retrieval and citations.
AI Agent Project
Build a customer support agent using RAG and API tools.
Agentic AI Capstone
Connect supervisor and worker agents, MCP, human approval and evaluation.
Choose how you learn
Generative AI & Agentic AI training near you and online.
Online training
Join from India, the USA, Canada, UK, Australia, New Zealand and Germany. Confirm your batch and time-zone schedule.
Classroom training
LearnAI Classroom: Dilsukhnagar, Hyderabad. Confirm this course, batch and exact address before visiting.
Hybrid training
Discuss combined online and classroom participation at your preferred branch. Confirm the delivery arrangements for your batch.
Fees, duration, prerequisites and upcoming batches are available on enquiry.
Learn with professional guidance
Experienced trainers. Practical knowledge.
Working professionalsIndustry knowledge connected to practical learning10+ yearsRelevant professional experience within our core trainer teamExperienced trainersGuidance for concepts, tasks and project work
LearnAI’s core trainer team is supported by additional and part-time trainers. Trainer assignments and relevant experience vary by course, branch, training mode and batch availability. Experience figures refer to professional experience, not years of teaching.
For your batch’s trainer details, contact LearnAI.
Plan your next step
Career directions and learning support.
Roles to explore
Career directions listed in the brochure. Entry requirements depend on the role, employer and your background.
Generative AI DeveloperLLM EngineerRAG EngineerAI Agent DeveloperAgentic AI EngineerAI Application DeveloperAI Automation EngineerAI Solutions Engineer
Discuss career assistance
Ask about resume preparation, ATS compatibility, job-description matching, cover letters, interview practice, job discovery and application support. Confirm current availability and what is included with your enrolment.
Enrolled students receive access details for their applicable LearnAI learning and career assistance applications.
Review the curriculum and ask for current delivery and enrolment details.
What does the LearnAI Generative AI & Agentic AI Course cover?
The curriculum has 33 modules. Build foundations in Python, NLP, machine learning and transformers, then explore LLM applications, prompts, retrieval-augmented generation, AI agents and multi-agent workflows. The curriculum includes memory, MCP, human approval, evaluation, guardrails and deployment.
Which tools and technologies are included?
The curriculum lists Python, OpenAI, Claude, Gemini, Ollama, Chroma, FAISS, LangChain, LangGraph, MCP, FastAPI, Docker, Hugging Face, Git & GitHub. Ask LearnAI about software setup and any tool accounts required for your batch.
Which projects are listed?
Project topics include GenAI Application, Enterprise RAG, AI Agent Project, Agentic AI Capstone. Confirm datasets, guidance and project selection with LearnAI.
Does the course include RAG, MCP and multi-agent systems?
Yes. The five phases include RAG, agent tools and memory, Model Context Protocol, multi-agent workflows, human approval, guardrails, evaluation and LLMOps. The curriculum also includes project modules.
Can I join online from outside India?
Yes. LearnAI offers online classes for learners in India and abroad, including the USA, Canada, UK, Australia, New Zealand and Germany. Ask about Generative AI & Agentic AI batch availability and your time-zone schedule.
Where can I attend classroom or hybrid training?
LearnAI is in Dilsukhnagar, Hyderabad. Confirm Generative AI & Agentic AI availability, delivery mode and the exact address before visiting.
What are the fees, duration and prerequisites?
Contact LearnAI on WhatsApp at +91 93900 23585 for current fees, duration, prerequisites and batch details. Share your background and preferred training mode.
What career assistance can I ask about?
Discuss resume support, job-description matching, interview practice, job discovery and application assistance. Confirm current availability and enrolment terms with LearnAI.
Discuss your learning goals
Ready to explore Generative AI & Agentic AI?
Share your background, preferred training mode, branch or country, and time zone.
