Python & API Foundations Build
A FastAPI service with JSON handling and Postman-tested endpoints, versioned on GitHub — the groundwork every later project sits on.
- Python
- FastAPI
- Postman
Best After 12th 4-Month Artificial Intelligence Program in Phagwara
Rated on Google4.9556+ reviews
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 Course in Phagwara
This is the 4-Month Artificial Intelligence course — it takes you from Python fundamentals to a deployed, portfolio-ready AI application. The flow is simple: 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.
Key Highlights
- Duration:
- 4 Months
- Eligibility:
- 12th Pass (Any Stream)
- Mode:
- Classroom, Weekend & 1-on-1
- Includes:
- Certificate + Placement Support
Overview
Course overview
This is the 4-Month Artificial Intelligence course — it takes you from Python fundamentals to a deployed, portfolio-ready AI application. The flow is simple: 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 final part 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
- 02
Understand LLM internals: tokenization, embeddings and attention
- 03
Write effective, structured prompts across multiple model APIs
- 04
Design and query vector databases for semantic search and RAG
- 05
Build AI agents using LangChain, LangGraph, CrewAI and MCP
- 06
Deploy AI applications with FastAPI, Docker and cloud platforms
- 07
Ship a complete AI industry capstone with a professional portfolio
SyllabusHands-on
Course Curriculum
The syllabus is arranged so every module produces an asset rather than a set of notes. You will cover month 1 — python, math & ai foundations, month 2 — deep learning, nlp & llm fundamentals, month 3 — prompting, llm apis, rag & ai agents, month 4 — ai apps, deployment & capstone, and finish with a live project built on Python, VS Code, Git & GitHub. Modules run in the order a real project runs: foundations first, then the core skills, then applied work under supervision, then the portfolio and interview preparation that turn all of it into an offer letter.
Artificial Intelligence02/04
Core Skills
The working knowledge the job description actually lists.
Month 2 — Deep Learning, NLP & LLM Fundamentals
Networks written and trained, then the internals of the models everyone else only calls.
4 weeks · 24 sessions
Topics covered
Deep learning fundamentals with PyTorchTensor operations and neural networksConvolutional neural networks and transfer learningComputer vision with OpenCVText processing and word embeddingsTransformers, Hugging Face and tokenizersLLM fundamentals: tokenization, embeddings and context windowsThe attention mechanism
The toolchain
Tools you will actually work in
Everything below is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
- Python
- VS Code
- Git & GitHub
- GitHub Copilot
- NumPy & Pandas
- scikit-learn
- PyTorch
- OpenCV
- Hugging Face
- OpenAI, Gemini, Claude & Grok APIs
- Ollama & LiteLLM
- FAISS, ChromaDB, Pinecone & Qdrant
- LangChain, LangGraph & CrewAI
- FastAPI, Streamlit, Gradio & Chainlit
- Whisper
- Docker
- AWS, Azure AI & Vertex AI
Eligibility
Who can do this course
- 01
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 Phagwara college using the weekday or weekend batch.
- 02
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.
- 03
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.
- 04
Developers and analysts
If you already write Python or work with data, the foundation topics move quickly and the LLM, RAG and agent sections are the point. Those are the skills currently missing from almost every engineering team.
The case for itWhy this programme
Why this programme
is worth your year
Modern AI work is a stack rather than a subject — a model, a retrieval layer, an agent loop, an interface, a deployment and guardrails — and the people who can assemble all six layers are still very thinly spread. That gap is the whole argument for this course: there is local demand, there are budgets, and there are very few trained people to hand the work to.
Build skills that hold up beyond the classroom.
What separates this from a playlist of tutorials is supervision on real work. From the second half of the course you build on live client projects with a trainer beside you, make decisions that have consequences, and correct them the following week. That loop is the skill. No employer in Phagwara will take your word for it without work they can inspect.
Be realistic about the money. A fresher who finishes with a working portfolio starts near the bottom of the band and moves quickly; someone who finishes with a certificate and nothing to show does not. The difference is entirely what you built.
The alternative is what most people try first: free videos, a cheap online course, six months of drifting, and knowledge you cannot demonstrate. A structured programme with live projects, a mentor who corrects you, an internship letter and a placement cell that actually calls employers is the difference between knowing the subject and being hired to do it.
Students reach the Phagwara centre from Banga, Nakodar, Kartarpur and the university belt, and the weekend batch exists so a job or a degree does not have to be paused to attend.
- 01
Python first, then models
The first month covers 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.
- 02
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.
- 03
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.
- 04
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.
Why now
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.
- A fresher with a deployed AI application typically starts around ₹20,000 – ₹40,000 per month in the Phagwara, Jalandhar and Ludhiana market.
- AI work also carries more remote and freelance opportunity than most, since the systems are not in the room.
Certification
Get Certified in Artificial Intelligence
Complete the course with a portfolio of live projects and receive an industry-recognised certificate, plus a documented internship letter accepted by Punjab universities.
- Industry Certificate
Recognised by employers across Punjab and beyond
- Internship Letter
Based on real client work, not a simulation
- Portfolio of Projects
Live work you can show in any interview
- Placement Support
CV review, mock interviews and hiring drives
Two certificates on completion — the course certificate and a separate capstone project certificate.
Future scope
Where this course takes you
The roles this opens, what they pay in Punjab and beyond, and who is hiring for them — drawn from published job-market listings, 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. The middle of the course is 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.
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.
- 07Prompt Engineer
- 08NLP Engineer
- 09Freelance AI Consultant
Salary outlook — AI Engineer
Builds and ships systems with models inside them. AI work carries more remote and freelance opportunity than most fields, since the systems are not in the room.
- Starting
- ₹20,000–₹40,000/month
- After 2 years
- ₹45,000–₹90,000/month
Indicative ranges compiled from public job-market listings. Actual offers vary by employer, skillset and interview performance.
Who is hiring for this
- Product companies building AI features into their software
- IT services companies adding AI delivery to their offering
- Analytics and consulting firms building client AI solutions
- Remote roles with companies outside Punjab and outside India
Portfolio
Hands-on projects you will ship
Computer Vision Model in PyTorch
Tensor operations, a convolutional network and transfer learning applied to a real image task with OpenCV, trained rather than described.
- PyTorch
- OpenCV
Multi-Provider Prompt Suite
Structured prompts and system prompts tested across OpenAI, Gemini, Claude, Grok and local Ollama models, routed through LiteLLM and compared on cost and quality.
- LiteLLM
- Ollama
RAG Pipeline with Guardrails
Embeddings into a vector database, hybrid search and re-ranking on top, evaluated properly and fenced with guardrails before anything is exposed.
- FAISS
- ChromaDB
- Qdrant
Tool-Calling AI Agent
A multi-agent system built with LangGraph and CrewAI over MCP tool definitions — the pattern behind every AI product currently being funded.
- LangGraph
- CrewAI
- MCP
End-to-End AI Industry Capstone
One complete, industry-level AI application integrating LLMs, a RAG pipeline, AI agents and cloud deployment, with project documentation, a GitHub portfolio, a resume and mock interviews around it.
- FastAPI
- Docker
- Vertex AI
The working loop
Learn it. Build it. Make it yours.
Every project moves through the same loop: understand the brief, build with guidance, then explain the decisions behind your work.
- 01
Understand
Take a real requirement apart before touching a tool — what is being asked, what it needs, and which part to build first.
Python & API Foundations Build
- 02
Build
Work hands-on with your trainer watching the screen, so a wrong turn is caught in the same session rather than three weeks later.
Computer Vision Model in PyTorch
- 03
Present
Walk through what you built and why you built it that way. This is the interview rehearsal, run against every project rather than once at the end.
Multi-Provider Prompt Suite
Why techcadd
Why students choose techcadd
There are many places to learn this in Phagwara and the brochure syllabus looks similar at all of them. What differs is who teaches, whether you ever touch real work, and whether anyone picks up the phone after you have paid. techcadd has trained students across Punjab since 2007 on the same model: small batches, working practitioners as trainers, client projects as coursework.
Learn at your own pace
You advance when a deliverable passes review. A student who needs extra time 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 month includes project documentation, a GitHub portfolio, resume building and mock interviews — the part most AI courses leave to the student.
FAQs
Frequently asked questions
Find answers to the questions students ask before enrolling.
Four months, covering the full AI syllabus across four phases: 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 content, and 1-on-1 training is available. Every class runs for 2 hours.
Course information
Ask about Artificial Intelligence
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.
- Free counselling and demo class
- Weekday, evening, weekend or 1-on-1, all 2-hour classes
- Internship letter and placement support
Get started today
Not sure if Artificial Intelligence 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.