Best After 12th 4-Month Artificial Intelligence Program in Jalandhar
A fast-track path into modern AI — Python for AI, deep learning, LLM internals, prompt engineering, RAG, AI agents and the deployment of real AI applications, ending in one complete industry capstone.
Artificial Intelligence Program Course in Jalandhar
Enrol in the Artificial Intelligence Mastery Program after 12th at techcadd Jalandhar (4.9★, 556+ reviews). Sixteen modules from Python and PyTorch through transformers, prompt engineering, multi-model LLM APIs, vector search, RAG, LangChain and CrewAI agents, to FastAPI apps deployed on Docker and the cloud, ending in an industry capstone.
Key Highlights :
- Duration: 4 Months
- Modules: 16
- Eligibility: 12th Pass, Any Stream
- Includes: Certificate + Placement Support
Course Overview
This is the Artificial Intelligence Mastery Program, 2026 edition: sixteen modules that run from Python fundamentals to a deployed, portfolio-ready AI application. The arc is deliberate — AI and Python foundations first, then deep learning, NLP and LLM internals, then prompting, multi-model APIs and retrieval, and finally application development, deployment and the capstone.
Nothing here stops at theory. You work in PyTorch rather than reading about neural networks, you call OpenAI, Gemini, Claude, Grok and local Ollama models rather than comparing them on a slide, you design and query real vector databases, and you build agents with LangChain, LangGraph, CrewAI and MCP. The last module packages all of it into an end-to-end build with documentation, a GitHub portfolio, a resume and mock interviews behind it.
What You'll Learn
Every module ends in something you have built and a trainer has reviewed, so the list below is work you will have done rather than topics you will have heard about.
- 01
Build neural networks and computer vision models using PyTorch
Tensor operations, neural networks, CNNs, transfer learning and OpenCV — written and trained rather than described.
- 02
Understand LLM internals: tokenization, embeddings and attention
Why a context window costs what it costs, why token boundaries break code, and what attention is actually doing underneath a chat interface.
- 03
Write effective, structured prompts across multiple model APIs
System prompts, structured prompting and prompt optimization, applied across OpenAI, Gemini, Claude, Grok and local Ollama models.
- 04
Design and query vector databases for semantic search and RAG
Embeddings into FAISS, ChromaDB, Pinecone and Qdrant, then hybrid search, re-ranking, evaluation and guardrails on top.
- 05
Build AI agents using LangChain, LangGraph, CrewAI and MCP
Tool calling, single agents and multi-agent systems — the pattern behind every AI product currently being funded.
- 06
Deploy AI applications with FastAPI, Docker and cloud platforms
A real interface in Streamlit, Gradio or Chainlit, containerised and shipped to AWS, Azure AI or Google Vertex AI, including serverless.
- 07
Ship a complete AI industry capstone with a professional portfolio
One end-to-end application integrating LLMs, RAG and agents, with documentation, a GitHub portfolio, a resume and mock interviews behind it.
Course Curriculum
Sixteen modules in teaching order, following the source syllabus exactly: AI and Python foundations, then deep learning, NLP and LLM fundamentals, then prompting, LLM APIs and RAG, and finally AI application development, deployment and the capstone.
Python Fundamentals & AI Tools
- 1
Python fundamentals — the language from the ground up.
- 2
VS Code as the working environment.
- 3
Git and GitHub for version control.
- 4
ChatGPT and GitHub Copilot as development tools.
- 5
AI productivity basics.
Tools you will actually work in
Everything here is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
- VSGPTCh
Who can do this course
Students straight after 12th
Join from any stream. There is no assumed technical knowledge and no programming prerequisite. Most students run the programme alongside a degree at a Jalandhar college using the weekday or weekend batch.
Graduates and final-year students
If you are finishing a BCA, B.Sc, BBA or B.Tech, this is the shortest route from degree to an AI role. You enter placement season with a deployed AI application instead of a blank CV.
Career changers
The weekend batch exists for people already earning. This is a fast track by design — enough to become interview-ready for AI Engineer and AI Application Developer roles without leaving your current job first.
Developers and analysts
If you already write Python or work with data, the foundation modules move quickly and the LLM, RAG and agent modules are the point. Those are the skills currently missing from almost every engineering team.
Why this programme is worth your year
Python first, then models
The first four modules are Python, OOP, APIs and the maths — NumPy, Pandas, statistics and scikit-learn — before PyTorch appears. Every model you build afterwards is something you can reason about rather than copy.
Six model providers, not one
OpenAI, Gemini, Claude and Grok APIs, plus Ollama for local models and LiteLLM to route between them. Knowing which model a task actually needs is a skill worth more than fluency in any single API.
Retrieval and agents, properly
Embeddings and four vector databases, then RAG architecture with hybrid search, re-ranking, evaluation and guardrails — then LangChain, LangGraph, CrewAI and MCP for tool-calling and multi-agent systems.
It ends deployed, not demonstrated
FastAPI, Streamlit, Gradio and Chainlit for the interface; Docker, AWS, Azure AI and Google Vertex AI for the deployment. An industry capstone with documentation, a GitHub portfolio and mock interviews closes the programme.
Build with AI. Ship it for real.
- Modern AI work is a stack, not a subject: a model, a retrieval layer that grounds it in your own data, an agent loop that lets it act, an interface people can use, a deployment that survives traffic, and guardrails for the day a prompt injection arrives.
- This programme teaches all six layers in sequence, with a single capstone that integrates LLMs, RAG pipelines, AI agents and cloud deployment into one application you can put your name on.
Get certified in Artificial Intelligence Program
Complete the course with a portfolio of live projects and receive an industry-recognised certificate, plus a documented internship letter accepted by Punjab universities.
Computer Education · JalandharCertificateof Project ExcellenceThis is to certify thatStudent Namehas designed, built and deployed a live capstone project in Artificial Intelligence Program, reviewed and graded under industry mentorship.
Computer Education · JalandharCertificateof Course CompletionThis is to certify thatStudent Namehas successfully completed the professional training programme in Artificial Intelligence Program with a grade of A+.
Two certificates on completion — the course certificate and a separate capstone project certificate.
Where this course takes you
The roles this opens, what they pay in Punjab and beyond, and who is hiring for them — the same figures our free Salary Estimator publishes, not a brochure number.
The core destination from this programme. You build and ship systems with models in them — retrieval, agents, APIs and deployment — rather than training models from scratch. Show: the capstone, the RAG pipeline and the deployed application.
Closer to the model than the product: PyTorch, neural networks, computer vision and transfer learning, with scikit-learn and the statistics behind them. Modules 3 to 6 are this role's foundation.
Owning the model layer of a product — tokenization, embeddings, context windows, multi-provider routing through LiteLLM, and the cost and latency decisions that follow. Modules 7, 9 and 14 are exactly this work.
Building systems that act rather than answer: tool calling, LangChain and LangGraph, CrewAI, MCP and multi-agent coordination. This is currently the hardest AI role for companies to fill.
The full product: FastAPI behind a Streamlit, Gradio or Chainlit interface, containerised with Docker and deployed to AWS, Azure AI or Vertex AI. The capstone is the portfolio piece this interview asks for.
The entry point at a services company or product team — supporting a live AI feature, curating evaluation data, tuning prompts and fixing the retrieval quality nobody else has time to look at.
Why students choose techcadd
Nine campuses across Punjab, 4.9★ from 556+ reviews, and a syllabus that is republished each year rather than reprinted.
Capability-gated, not calendar-gated
You advance when a deliverable passes review. A student who needs an extra week on transformers gets it; nobody is moved on because the timetable says so.
Real model APIs, with budgets
Labs run against live OpenAI, Gemini, Claude and Grok endpoints with per-student token budgets, plus local models through Ollama — so the cost of a design decision is something you have felt.
Trainers who still ship
The people teaching RAG evaluation and agent orchestration are the people writing them for client work, which is why the guardrails sections cover failures that actually happen.
Portfolio and interview support
The final module is explicitly project documentation, a GitHub portfolio, resume building and mock interviews — the part most AI courses leave to the student.
Frequently asked questions
Find answers to the questions students ask before enrolling.
Four months, covering the full sixteen-module Artificial Intelligence Mastery syllabus across four stages: AI and Python foundations, then deep learning, NLP and LLM fundamentals, then prompting, LLM APIs and RAG, and finally AI application development, deployment and the capstone. Weekday, evening and weekend batches cover the same modules, and 1-on-1 training is available if you would rather set your own pace. Every class runs for 2 hours, whichever format you choose.
No. Module 1 begins with Python fundamentals and the tooling around them, and Modules 2 and 3 add object-oriented programming, APIs and the maths — NumPy, Pandas, statistics and scikit-learn — before PyTorch appears. The programme is built for students joining straight after 12th from any stream.
Data science is largely about analysing data you already have. This programme is about building software with models inside it: deep learning in PyTorch, LLM internals, prompting across several providers, retrieval-augmented generation over your own documents, agents that call tools, and a deployed application at the end.
OpenAI, Gemini, Claude and Grok through their APIs, local models through Ollama, and LiteLLM to route between them. On top of that: PyTorch and Hugging Face for models you train or fine-tune, and Whisper and vision-language models for the multimodal module.
RAG grounds a model's answers in your own documents rather than in whatever it memorised — embeddings, a vector database, hybrid search, re-ranking and guardrails. An agent goes further: it plans its own next step, calls a real tool, reads the result and repeats. Modules 10 to 12 build both.
One complete, industry-level AI application — the capstone — integrating LLMs, a RAG pipeline, AI agents and cloud deployment, with project documentation, a GitHub portfolio, a resume and mock interviews around it. Every module before it contributes a working piece of that build.
The syllabus targets nine roles: AI Engineer, Machine Learning Engineer, LLM Engineer, AI Agent Developer, Prompt Engineer, AI Application Developer, NLP Engineer, Junior AI Developer and Freelance AI Consultant. Which one fits depends on whether you lean toward the model, the product or the agent layer — and the capstone is the evidence all nine interviews ask for.
A fresher with a deployed AI application typically starts around ₹20,000 – ₹40,000 per month in the Jalandhar and Ludhiana market, rising quickly with a second year of production experience. AI work also carries more remote and freelance opportunity than most, since the systems are not in the room.
No training provider can honestly guarantee a job, and you should be cautious of anyone in Jalandhar who claims one. techcadd guarantees placement support: CV reviews, mock interviews, portfolio preparation and repeated drives with hiring partners across Jalandhar and Ludhiana.
Yes. The programme ends in a documented final evaluation — capstone demonstration, technical viva and certification — and every student receives an industry-recognised certificate on completion alongside a documented internship letter based on live project work.
Ask about Artificial Intelligence Program
Send your question and a counsellor will call you back about batch timings, fees, EMI options, placement record, or whether this course fits your degree.
- info@techcadd.com
- Phone
- +91 98881 22254
- Address
- 2nd Floor, Crystal Plaza, SCS 78, Opposite PIMS Hospital, Jalandhar, Punjab 144001
- Counselling hours
- Monday – Saturday, 9:00 AM – 7:00 PM
Not sure if Artificial Intelligence Program is the right fit?
One call with a counsellor is usually enough to find out. Book a free demo class and see the lab before you decide.