Artificial Intelligence · for Ameerpet learners
Generative AI + Agentic AI, Agentic AI and Data Science + GenAI + Agentic AI — taught hands-on at our KPHB centre, a direct Red Line Metro ride from Ameerpet, or live online.
Why The Data School
Start with Data Science + GenAI + Agentic AI — it begins with Python basics.
Generative AI + Agentic AI: prompt engineering, embeddings, RAG, AI apps and agents.
Agentic AI: tool calling, workflows, memory and multi-agent systems.
Data Analytics + GenAI + Agentic AI: Excel, SQL, Power BI and AI-powered reporting.
We're in KPHB Colony, Kukatpally — easy to reach without a car:
Every AI program ends with portfolio projects — a RAG document assistant, a research agent, a multi-agent business assistant or an ML model — that you can demo in interviews.
Batches run Monday to Saturday, 9:30 AM – 11:30 AM or 11:30 AM – 1:30 PM.
Programs
Build LLM apps with prompt engineering, embeddings and RAG — then design AI agents that use tools and run multi-step workflows.
13 modules · 77 topics
See syllabusMaster Python, statistics, SQL, machine learning and deep learning — then build LLM apps, RAG systems and AI agents.
16 modules · 102 topics
See syllabusBuild, deploy and run AI solutions directly with customers — engineering, data, LLMs, agents, cloud and client communication.
11 modules · 66 topics
See syllabusDesign intelligent agents that reason, use tools and execute multi-step workflows.
8 modules · 46 topics
See syllabusWhat you'll learn
A look inside our most popular AI program. Compare all AI programs above.
Build an accurate mental model of what generative AI can and cannot do.
What generative AI is · Transformers and LLMs at a high level · Tokens and context windows · Capabilities and limits · Hallucinations
Write, test and reuse prompts that produce consistent, useful output.
Prompt structure and roles · Zero-shot and few-shot prompting · Chain-of-thought · Structured outputs (JSON) · Iteration and testing
Learn the building blocks for integrating LLMs into software.
Calling LLM APIs · System prompts and parameters · Chaining calls · Function / tool calling · Streaming
Represent meaning as vectors to power search and classification.
What embeddings are · Generating embeddings via APIs · Similarity measures · Semantic search · Clustering and classification
Store and retrieve embeddings efficiently at application scale.
Vector database overview · Indexing and nearest-neighbour search · Chunking strategies · Metadata filtering · Hybrid search
Ground model answers in your own data and cite the sources.
RAG architecture · Ingestion pipelines · Retrieval and re-ranking · Context construction · Reducing hallucinations
Design and ship a complete AI application with a backend and a simple UI.
Designing an AI app · Backends with APIs · Simple UIs · Conversation memory · Configuration and secrets
Understand what makes a system agentic and when agents are the right tool.
Agents vs chatbots · The perceive-plan-act loop · Reasoning and planning patterns · Agent components · Use cases and limits
Give agents safe, well-defined abilities to act in the world.
Function calling · Tool schemas · External APIs · Database and search tools · Error handling
Structure multi-step work and help agents remember what matters.
Workflows vs autonomous agents · Task decomposition · Retries, fallbacks and human-in-the-loop · State management · Short vs long-term memory
Use retrieval as a tool and coordinate specialised agents on larger tasks.
Retrieval as a tool · Query rewriting and routing · Roles and delegation patterns · Supervisor / worker designs · Agent communication and shared state
Measure quality and keep LLM apps and agents safe and cost-controlled.
Evaluating LLM outputs and agent trajectories · Retrieval quality metrics and test sets · Prompt injection · Input / output guardrails and permission limits · Logging and observability
Scope, build, evaluate and present portfolio-ready generative and agentic AI projects.
Scoping · Building a RAG assistant · Research agent · Multi-agent business assistant · Documentation and demo
Who it's for
Outcomes depend on individual effort, background and the job market.
Visit us
Classroom training at our centre in KPHB Colony, Kukatpally — close to KPHB Metro, JNTU, Nizampet, Miyapur and Hitech City.
The Data School — KPHB, Hyderabad
4th Floor, MIG 549, Road Number 1, Above HDFC Bank, KPHB Colony Phase 2, Kukatpally, Hyderabad, Telangana 500072
Monday–Saturday, 9:00 AM – 9:00 PM (Sunday closed)
By Metro
Take the Red Line (Miyapur – LB Nagar) to KPHB Colony station, then head to Road Number 1 in KPHB Colony. Look for HDFC Bank — our centre is in the same building, above the bank.
From JNTU, Nizampet & Bachupally
Come into KPHB Colony and take Road Number 1. The centre is above HDFC Bank, near Prime Hospitals and opposite GHMC Park.
From Miyapur, Hitech City & Kondapur
Travel towards Kukatpally / KPHB Colony and turn onto Road Number 1. Use “Get directions” for turn-by-turn navigation to our Google Maps listing.
First visit?
Call +91 96664 47822 before you set off and we'll guide you in. Open Monday–Saturday, 9:00 AM – 9:00 PM (Sunday closed).
FAQ
Still unsure? An advisor is happy to help.
No. We're in KPHB Colony, Kukatpally (4th Floor, MIG 549, Road Number 1, Above HDFC Bank, KPHB Colony, Kukatpally, Hyderabad), a direct Red Line ride from Ameerpet. Online classes are available too.
New to programming: Data Science + GenAI + Agentic AI. Comfortable with Python: Generative AI + Agentic AI. Want a customer-facing deployment role: Forward Deployed Engineer.
The course fee is ₹30,000. Classroom batches run Monday to Saturday, 9:30 AM – 11:30 AM or 11:30 AM – 1:30 PM. You can attend a free demo class first — book one online or call the centre, and an advisor will share the next start date.
Yes — each program ends with portfolio projects you can demo.
Yes. Book one at the KPHB centre or online.
Speak with an advisor about the right program, learning modes, fees and upcoming batches.
