Best After 12th 6-Month Artificial Intelligence Program in Jalandhar
A project-driven path into modern AI — Python for AI, deep learning, NLP, LLM internals, prompt engineering, RAG, AI agents and the production deployment of AI applications, over twenty-four modules and six months.
Artificial Intelligence Certificate Program Course in Jalandhar
Enrol in the 6-month Artificial Intelligence Mastery Program after 12th at techcadd Jalandhar (4.9★, 556+ reviews). Twenty-four modules from Python and scikit-learn through PyTorch, NLP, Transformers, LLM internals, prompting, multi-model APIs, vector search, RAG, LangChain and CrewAI agents, FastAPI backends, multimodal AI, Docker and cloud deployment, ending in an industry capstone.
Key Highlights :
- Duration: 6 Months
- Modules: 24
- Eligibility: 12th Pass, Any Stream
- Includes: Certificate + Placement Support
Course Overview
This is the six-month track of the Artificial Intelligence Mastery Program, 2026 edition: twenty-four modules across six stages. Python and AI/ML foundations first, then deep learning and NLP, then LLM internals and prompting, then RAG and AI agents, then AI application development, and finally deployment, security and the capstone.
The extra length buys depth rather than filler. Machine learning gets a module of its own with model training, evaluation and cross-validation. NLP separates from Transformers. Chatbot design and multimodal AI separate from application development. AI security and responsible AI become a module rather than a bullet, and the final month adds documentation, a GitHub portfolio, resume building and mock interviews on top of the capstone build.
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 and multi-agent systems with LangChain and CrewAI
Chains, memory and prompt templates, then tool calling and MCP, then autonomous workflows and enterprise agent design.
- 06
Deploy AI applications with FastAPI, Docker and major cloud platforms
Async backends with background tasks and WebSockets, containerised behind Nginx 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, agents, Docker and cloud deployment, with documentation, a GitHub portfolio, a resume and mock interviews behind it.
Course Curriculum
Twenty-four modules in teaching order, following the source syllabus exactly: Python and AI/ML foundations, then deep learning and NLP, then LLM fundamentals and prompting, then RAG and AI agents, then AI application development, and finally 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
The uv package manager and virtual environments.
- 4
Git and GitHub for version control.
- 5
ChatGPT and GitHub Copilot as development tools.
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 version that changes which interviews you are invited to. You arrive with a deployed AI application, a GitHub portfolio and mock interviews behind you.
Career changers
The weekend batch exists for people already earning. Six months of evenings and Saturdays is enough to move into 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, agent and deployment modules are the point. Those are the skills currently missing from almost every engineering team.
Why this programme is worth your year
Engineering practice, not just scripts
Module 2 is OOP, exception handling, logging, type hinting and pytest before any model appears. It is the difference between code that ran once on your laptop and code a team can maintain.
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 worth more than fluency in any single API.
Agents get four modules, not a mention
RAG architecture, then LangChain and LangGraph, then CrewAI, MCP and tool calling, then multi-agent systems and enterprise agent design — the pattern behind every AI product currently being funded.
It ends deployed and defended
Docker, Nginx, AWS, Azure AI and Vertex AI for the deployment; prompt injection, jailbreak defence and secret management for the security module; then documentation, a GitHub portfolio and mock interviews.
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, a backend and interface people can use, a deployment that survives traffic, and guardrails for the day a prompt injection arrives.
- This programme teaches every one of those layers in sequence, with a single capstone that integrates LLMs, RAG pipelines, AI agents, Docker containerisation and full cloud deployment into one application you can put your name on.
Get certified in Artificial Intelligence Certificate 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 Certificate 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 Certificate 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, backends 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: scikit-learn, model training, evaluation and cross-validation, then PyTorch and neural networks. Modules 3 to 6 are this role's foundation and they get a full two months.
Neural networks, CNNs, transfer learning and computer vision with OpenCV, then Transformers and the Hugging Face Model Hub. The six-month track is the one that gives this its own two modules rather than a single pass.
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 9, 11 and 22 are exactly this work.
Building systems that act rather than answer: LangChain and LangGraph, CrewAI, MCP, tool calling, multi-agent systems and enterprise agent design. Four modules, and currently the hardest AI role to fill.
The full product: an async FastAPI backend with WebSockets 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.
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.
Placement prep is a module
The final module is explicitly project documentation, a GitHub portfolio, resume building, mock interviews and industry standards — the part most AI courses leave to the student.
Frequently asked questions
Find answers to the questions students ask before enrolling.
Six months, covering all twenty-four modules across six stages: Python and AI/ML foundations, deep learning and NLP, LLM fundamentals and prompting, RAG and AI agents, AI application development, and deployment with 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.
The shorter track is a fast path through the same arc in sixteen modules. This one is twenty-four, and the extra eight are depth rather than new topics: machine learning gets its own module with training, evaluation and cross-validation; NLP separates from Transformers; agents get four modules instead of one; chatbot design and multimodal AI separate from application development; AI security becomes a module; and placement preparation is added at the end.
No. Module 1 begins with Python fundamentals and the tooling around them, and Modules 2 to 4 add engineering practice, the maths and machine learning before PyTorch appears. The programme is built for students joining straight after 12th from any stream.
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 12 to 16 build both, including multi-agent systems.
One complete, industry-level AI application — the capstone — an LLM-powered app with RAG, AI agents, Docker containerisation and full cloud deployment, followed by a module of project documentation, a GitHub portfolio, resume building and mock interviews. Every module before it contributes a working piece of that build.
The syllabus targets nine roles: AI Engineer, Machine Learning Engineer, Deep Learning Engineer, LLM Engineer, AI Agent Developer, Prompt Engineer, NLP Engineer, AI Application 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 and a documented portfolio typically starts around ₹25,000 – ₹50,000 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 — and the final module is built around exactly that.
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 Certificate 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 Certificate 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.