01
Students Straight After 12th
From any stream, with zero assumed coding background — can run alongside a college degree using a weekday or weekend session.
Start right after school
After 12th 4-Month Artificial Intelligence Program in Mohali
A focused, four-month route into practical AI work — Python and machine-learning foundations, deep learning, large language models, retrieval-augmented generation, and AI agents — ending with one deployed application you can show in an interview, not just describe in one.
0113
01Where this starts
Course Overview
The programme is built the way the work is done: the applied-math and programming layer first, then the models, then the systems built around them, then deployment.
Months one and two put Python, engineering practice and the maths under your hands before any framework appears — NumPy, Pandas, statistics and scikit-learn, then neural networks, CNNs and the transformer mechanics behind every modern chatbot.
Months three and four are where it becomes employable work: prompting across four model providers, vector search and full RAG architecture, agent-building with LangChain, LangGraph, CrewAI and MCP, and finally a containerised application deployed to the cloud.
02Skills you collect
What You'll Learn
Six capabilities the four months are built around — each one something you can demonstrate, not just describe.
Write Python Like a Developer
Python from scratch with object-oriented programming, exception handling, Git and GitHub, APIs and JSON, and FastAPI basics — the engineering layer, not just syntax.
Build and Evaluate Models
NumPy, Pandas, statistics and probability, and scikit-learn fundamentals, then neural networks, CNNs and transfer learning in PyTorch.
Understand What an LLM Actually Does
Tokenization, embeddings, context windows and attention — the mechanics behind every modern chatbot, rather than prompting a black box.
Work Across Model Providers
Structured prompt engineering across OpenAI, Gemini, Claude and Grok APIs, plus local models via Ollama and routing between them with LiteLLM.
Ground a Model in Real Data
Vector databases, semantic search and full RAG architecture with hybrid search and re-ranking.
Ship It
Interfaces in Streamlit, Gradio or Chainlit, containerised with Docker and deployed to AWS, Azure AI or Google Vertex AI.
03The route
Program Structure: What Four Months Actually Covers
The course moves in a deliberate sequence — foundations before frameworks, understanding before frameworks, and building before deploying.
- Python programming from scratch, and the applied-math layer every AI course skips too fast.
- Python programming from scratch
- Git / GitHub
- Object-oriented programming
- Exception handling
- Working with APIs and JSON
- FastAPI basics
- NumPy
- Pandas
- Statistics and probability
- scikit-learn fundamentals
It Ends Deployed
Docker, cloud deployment and a documented capstone mean the final output is a working application with a live link, not a slide describing one.
Outcome
One complete, end-to-end AI application combining a language model, a RAG pipeline and an AI agent, deployed to the cloud with full documentation and a GitHub portfolio entry.
04Your toolkit
Tools You Will Work With
Four months across four model providers, four vector databases and three cloud platforms — because real product teams rarely commit to just one.
PythonGit & GitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceOpenAIGeminiClaudeGrokOllamaLiteLLMPythonGit & GitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceOpenAIGeminiClaudeGrokOllamaLiteLLMPythonGit & GitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceOpenAIGeminiClaudeGrokOllamaLiteLLM
FAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerAWSAzure AIGoogle Vertex AIFAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerAWSAzure AIGoogle Vertex AIFAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerAWSAzure AIGoogle Vertex AI
- Python
- Month 1 — the language the whole course runs on.
- Git & GitHub
- Month 1 — version control and the portfolio it becomes.
- FastAPI
- Month 1 — the API layer behind your applications.
- NumPy
- Month 1 — arrays and the numeric layer under everything.
- Pandas
- Month 1 — loading, cleaning and analysing data.
- scikit-learn
- Month 1 — machine-learning fundamentals.
- PyTorch
- Month 2 — neural networks, CNNs and transfer learning.
- OpenCV
- Month 2 — computer vision.
- Hugging Face
- Month 2 — transformer models and tokenizers.
- OpenAI
- Month 3 — the first LLM API you call.
- Gemini
- Month 3 — a second provider to compare against.
- Claude
- Month 3 — a third, for long-context work.
- Grok
- Month 3 — a fourth provider in the same codebase.
- Ollama
- Month 3 — running models locally.
- LiteLLM
- Month 3 — routing between all of them.
- FAISS
- Month 3 — local similarity search.
- ChromaDB
- Month 3 — an embedded vector store.
- Pinecone
- Month 3 — the hosted vector database.
- Qdrant
- Month 3 — filtering alongside vector search.
- LangChain
- Month 3 — chains, prompts and tools.
- LangGraph
- Month 3 — stateful agent workflows.
- CrewAI
- Month 3 — multi-agent coordination.
- MCP
- Month 3 — Model Context Protocol for tool calling.
- Streamlit
- Month 4 — the fastest route to an interface.
- Gradio
- Month 4 — demo interfaces for models.
- Chainlit
- Month 4 — chat interfaces for AI apps.
- Docker
- Month 4 — containerising the application.
- AWS
- Month 4 — cloud deployment.
- Azure AI
- Month 4 — Microsoft's AI platform.
- Google Vertex AI
- Month 4 — Google's managed AI platform.
05Is this you?
Who This Program Is For
02
Graduating BCA / B.Sc / B.Tech Students
Who want a deployed AI project to walk into placement season with, instead of a resume built only on coursework.
03
Working Professionals
In Mohali's IT and BPO sector, looking to move into AI-adjacent roles via the weekend track, without quitting a current job first.
04
Junior Developers or Data Analysts
Who already write some Python — the early foundation moves fast for this group, and the LLM/RAG/agent modules are the real destination.
06Why this one
What Makes the Curriculum Different
01
Six model providers, not one
Working across OpenAI, Gemini, Claude, Grok and local Ollama models — plus LiteLLM for routing between them — matters more for employability than deep fluency in a single API, since real product teams rarely commit to just one provider.
02
Retrieval and agents are treated as core, not optional add-ons
A large share of current AI hiring in and around Mohali's IT Park is specifically for people who can build a working RAG pipeline or a tool-calling agent — not just prompt a chatbot.
03
It ends deployed
Docker, cloud deployment, and a documented capstone mean the final output is a working application with a live link, not a slide describing one.
07Why now
Why This Program Fits Mohali Specifically
Mohali's IT Park (Quark City and the surrounding sectors) and the wider tricity tech ecosystem have shifted hard toward AI-integrated product work over the past two years — companies aren't just hiring "developers" anymore, they're hiring people who can wire a large language model into a real application, ground it in company data, and ship it safely.
That shift has outpaced what most degree programs teach. A B.Tech or BCA syllabus in the region still moves slowly toward AI topics, while local product teams and IT-park companies are already hiring for LLM integration, RAG pipelines, and AI agent development today. This program is built to put a 12th-pass or early-degree student directly into that gap — with four months of hands-on build work instead of a four-year wait.
What local teams are hiring for
- LLM integrationMonth 3 — four provider APIs, local models and routing between them.
- RAG pipelinesMonth 3 — vector databases, hybrid search and re-ranking.
- AI agent developmentMonth 3 — LangChain, LangGraph, CrewAI and MCP tool calling.
- Grounding a model in company dataMonth 3 — full RAG architecture over your own documents.
- Shipping it safelyMonth 4 — containerised, deployed and documented, not left on a laptop.
Talk to a Course Advisor
Ten minutes with the techcadd team settles eligibility, session timings, fees and which of the three AI tracks fits — before you commit four months to it.
08Certification
Certification & Placement Support
Students receive an industry-recognised course completion certificate and a separate capstone project certificate, along with a documented internship letter based on real project work. Placement support includes CV review, mock interviews, portfolio preparation, and hiring drives with partner companies across the tricity region.
Course Completion CertificateAn industry-recognised certificate for completing the four-month programme.
Capstone Project CertificateA separate certificate for the end-to-end AI application you build and deploy.
Internship LetterA documented internship letter based on real project work.
Placement SupportCV review, mock interviews, portfolio preparation, and hiring drives with partner companies across the tricity region.
Capstone Defense
Fullscreen
Project Completion Certificate
ISO 9001:2015 Accredited
Fullscreen
Course Completion Certificate
Two documents on completion — the course certificate, and a separate certificate for the capstone you build and defend. Click any card to inspect in full screen.
09Where it leads
Career Paths This Opens
Six roles the four months point at, from the model layer through to the deployed product.
Role and what it involves
Building and shipping systems with models embedded — retrieval, agents, APIs, deployment.
10What you build
What You Build Along the Way
01
First API Service
A working FastAPI service that reads and returns JSON, version-controlled on GitHub. Month 1 · Python · FastAPI · Git
02
Machine Learning Model
A trained and evaluated scikit-learn model over a real dataset prepared with NumPy and Pandas. Month 1 · scikit-learn · Pandas · NumPy
03
Computer Vision Build
A CNN in PyTorch with transfer learning, applied to images through OpenCV. Month 2 · PyTorch · OpenCV
04
RAG Pipeline
A retrieval system over your own documents with a vector database, hybrid search and re-ranking. Month 3 · FAISS/Chroma/Pinecone/Qdrant
05
Tool-Calling Agent
An agent that uses tools and completes a multi-step task, built with LangGraph, CrewAI and MCP. Month 3 · LangChain · LangGraph · CrewAI · MCP
06
End-to-End AI Capstone
One complete application — LLMs, RAG and agents integrated, containerised and deployed to the cloud, backed by documentation, a GitHub portfolio and mock interviews. Month 4 · Docker · AWS / Azure AI / Vertex AI
11How it works
Foundations. Frameworks. Build. Deploy.
Foundations before frameworks, understanding before frameworks, and building before deploying — applied to every topic in the course.
Foundations
The programming, engineering practice and applied maths under the topic, before any library is introduced.
First API ServiceUnderstanding
What the model or system is actually doing — tokenization, attention, retrieval — before a framework hides it.
Machine Learning ModelBuild
Write the thing yourself, with the framework, until it works on your own data.
Computer Vision BuildDeploy
Containerise it, put it on a cloud platform, document it, and be able to hand over the link.
RAG Pipeline12Why techcadd
Built for the Gap Mohali Is Hiring Into
Four months of hands-on build work aimed at what local product teams and IT-park companies are recruiting for today.
01
Four Months, Not Four Years
Built to put a 12th-pass or early-degree student directly into the gap between what degree syllabi teach and what local teams are hiring for.
02
Practical-First
Theory runs alongside the build work rather than ahead of it, so every concept lands against something you are making.
03
No Assumed Background
It starts from Python fundamentals, adding OOP, APIs and applied maths before any machine-learning topic appears.
04
Sessions That Fit Around a Degree
Weekday, evening and weekend options exist specifically so the programme can run in parallel with ongoing BCA, B.Sc or B.Tech coursework.
05
A Portfolio, Not a Transcript
You finish with a deployed application, documentation and a GitHub portfolio entry to take into placement season.
13Who has walked it
Students who started where you are
Alumni of this route, on what changed once they were sitting in interviews.
4.8
190 reviews
586%
49%
33%
21%
11%
Before joining the Artificial Intelligence Course in Mohali, I was confused about where to start with AI. The trainers explained Python, Machine Learning and AI concepts step by step. The practical assignments helped me understand the topics much better.
I had very little knowledge of Artificial Intelligence when I enrolled. The course started with the basics and gradually moved toward Machine Learning, Deep Learning and Generative AI. The learning process felt manageable and well structured.
What I liked most was the project-based learning. Instead of only studying algorithms, I got opportunities to work on practical AI applications. It helped me understand how the concepts are actually used in projects.
I wanted to learn AI but was not confident in Python. The initial modules helped me strengthen my programming basics before moving into Machine Learning. That made the advanced topics much easier to understand.
The Generative AI and LLM modules were the most interesting part for me. Learning about prompt engineering, LLM applications and modern AI workflows gave me a better idea of how this technology is being used today.
I liked the trainer interaction during practical sessions. Whenever I got stuck with code or a Machine Learning model, I could discuss the issue and understand where I was making mistakes. That support made self-learning much easier.
I am from a technical background and wanted to add AI skills to my existing knowledge. The course gave me exposure to Python, Machine Learning, Deep Learning and Computer Vision in a structured way.
The course encouraged us to work on projects rather than simply completing theory. Building AI applications helped me understand how to present my technical work and gave me useful material for my portfolio.
I joined because I wanted to explore AI as a career option. The course helped me understand the difference between AI, Machine Learning and Deep Learning and showed me which skills I should focus on next.
I was specifically looking for an AI training course that covered more than traditional Machine Learning. The inclusion of NLP, Generative AI, LLMs and Computer Vision made the curriculum more interesting for me.
There are so many AI tutorials online that I didn't know what to learn first. Having a proper sequence from Python and data handling to Machine Learning and advanced AI topics made my learning journey much more organised.
The overall learning environment was comfortable for asking questions and practising concepts. As someone from the Chandigarh Tricity region, having access to a local AI training option made regular learning more convenient.
14Before you go
Frequently Asked Questions
Still unsure? A ten-minute call with a counsellor usually settles it faster than any brochure.
Yes. It starts from Python fundamentals in Month 1, adding object-oriented programming, APIs, and applied math before any machine-learning or deep-learning topic is introduced.
15Final stage
Ask About the Artificial Intelligence Program in Mohali
Want to know which of the three AI tracks — four months, six months or the nine-month diploma — fits your background and the time you have?
Speak with a course advisor about session timings, fees, eligibility, the capstone and the placement support that comes with it.
Location: Mohali, Punjab.
+91 98881 22442Call the admissions desk+91 98881 22442WhatsApp for a quick replyinfo@techcadd.comEmail us your questions
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Still deciding
Not Sure If Four Months Is the Right Length?
A counselling session can help you compare the four-month programme against the six-month certificate and the nine-month diploma before you commit.
All three start from Python fundamentals with no assumed coding background — the difference is how much depth there is room for, and how far past the first deployed application you go.
Get Started Today
- 4 months, 12th pass, any stream
- No assumed coding background — Python from scratch
- PyTorch, CNNs, transfer learning and computer vision
- Four model providers plus local models and routing
- Vector databases, full RAG, LangGraph, CrewAI and MCP
- Docker and deployment to AWS, Azure AI or Vertex AI
- One deployed capstone, documented, with a GitHub portfolio