Python & ML Foundations Build
A typed, tested Python project with a FastAPI endpoint and a scikit-learn model trained, evaluated and cross-validated properly.
- Python
- FastAPI
- scikit-learn
Best After 12th 6-Month Artificial Intelligence Program in Phagwara
Rated on Google4.9556+ reviews
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, spread across six hands-on months.
Artificial Intelligence Course in Phagwara
This is the six-month Artificial Intelligence course: 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.
Key Highlights
- Duration:
- 6 Months
- Eligibility:
- 12th Pass (Any Stream)
- Mode:
- Classroom, Weekend & 1-on-1
- Includes:
- Certificate + Placement Support
Overview
Course overview
This is the six-month Artificial Intelligence course: 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 compared to shorter courses buys depth rather than filler. Machine learning gets its own dedicated time 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 get proper coverage, 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
- 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 and multi-agent systems with LangChain and CrewAI
- 06
Deploy AI applications with FastAPI, Docker and the major 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 & ai/ml foundations, month 2 — deep learning & nlp, month 3 — llm fundamentals & prompting, month 4 — rag & ai agents, and finish with a live project built on Python & uv, VS Code, Git, GitHub & Copilot. 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 3 — LLM Fundamentals & Prompting
Why a context window costs what it costs, and how to write prompts that hold up.
4 weeks · 24 sessions
Topics covered
LLM fundamentals: tokenization, embeddings and context windowsThe attention mechanismPrompt engineering, prompt optimisation and system promptsStructured promptingThe OpenAI, Gemini, Claude and Grok APIsOllama for local models and LiteLLM for routing between providers
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 & uv
- VS Code
- Git, GitHub & Copilot
- pytest
- NumPy, Pandas & scikit-learn
- PyTorch & OpenCV
- Hugging Face & Transformers
- OpenAI, Gemini, Claude & Grok APIs
- Ollama & LiteLLM
- FAISS, ChromaDB, Pinecone & Qdrant
- LangChain, LangGraph, CrewAI & MCP
- FastAPI, Streamlit, Gradio & Chainlit
- Whisper & vision-language models
- Docker, Linux & Nginx
- 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 version that changes which interviews you are invited to. You arrive with a deployed AI application, a GitHub portfolio and mock interviews behind you.
- 03
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.
- 04
Developers and analysts
If you already write Python or work with data, the foundation topics move quickly and the LLM, RAG, agent and deployment months 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, a backend, a deployment and guardrails — and the skills for the middle four layers are missing from almost every engineering team. 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
Engineering practice, not just scripts
OOP, exception handling, logging, type hinting and pytest are taught early, before any model appears. It is the difference between code that ran once on your laptop and code a team can maintain.
- 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 worth more than fluency in any single API.
- 03
Agents get four full topics, 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.
- 04
It ends deployed and defended
Docker, Nginx, AWS, Azure AI and Vertex AI for the deployment; prompt injection, jailbreak defence and secret management for security; then documentation, a GitHub portfolio and mock interviews.
Why now
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.
- A fresher with a deployed AI application and a documented portfolio typically starts around ₹25,000 – ₹50,000 per month in this 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, backends and deployment — rather than training models from scratch.
Closer to the model than the product: scikit-learn, model training, evaluation and cross-validation, then PyTorch and neural networks. That block gets a full two months here.
Neural networks, CNNs, transfer learning and computer vision with OpenCV, then Transformers and the Hugging Face Model Hub. The six-month course is the one that gives this dedicated depth.
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: LangChain and LangGraph, CrewAI, MCP, tool calling, multi-agent systems and enterprise agent design. Currently the hardest AI role to fill.
The full product: an async FastAPI backend with WebSockets behind a Streamlit, Gradio or Chainlit interface, containerised and deployed. The capstone is the portfolio piece this interview asks for.
- 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
- ₹25,000–₹50,000/month
- After 2 years
- ₹55,000–₹1,10,000/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 client work
- Analytics and consulting firms building AI solutions
- Remote roles with companies outside Punjab and outside India
Portfolio
Hands-on projects you will ship
Deep Learning & NLP Build
A PyTorch CNN with transfer learning and OpenCV, plus a text pipeline through word embeddings and sequence models into Transformers and Hugging Face.
- PyTorch
- OpenCV
- Hugging Face
Multi-Provider Prompt Application
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 & Multi-Agent System
Embeddings into a vector database with hybrid search, re-ranking, evaluation and guardrails, then agents built with LangGraph, CrewAI and MCP tool calling.
- FAISS
- LangGraph
- CrewAI
Conversational & Multimodal AI App
An async FastAPI backend with WebSockets behind a Streamlit or Chainlit interface, with dialogue management, Whisper speech input and a vision-language model.
- FastAPI
- Chainlit
- Whisper
End-to-End AI Capstone
An LLM-powered application with RAG, AI agents, Docker containerisation and full cloud deployment, hardened against prompt injection and delivered with documentation and a GitHub portfolio.
- Docker
- Nginx
- AWS
- 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 & ML 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.
Deep Learning & NLP Build
- 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 Application
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.
Placement prep is built in
The final month includes project documentation, a GitHub portfolio, resume building, mock interviews and industry standards — the part most AI courses leave to the student.
FAQs
Frequently asked questions
Find answers to the questions students ask before enrolling.
Six months, organised month by month: 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 syllabus, 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.