Best After 12th 9-Month Artificial Intelligence Diploma Program in Jalandhar
A nine-month, project-driven path from data fundamentals to enterprise-grade AI systems — Python, data engineering, machine learning, deep learning, LLMs, RAG, AI agents and full production deployment, finishing on a complete AI SaaS capstone you build and ship yourself.
Artificial Intelligence Diploma Program Course in Jalandhar
techcadd's 9-month AI Mastery diploma in Jalandhar: 36 modules from Excel, Power BI, Python and SQL through machine learning, deep learning and computer vision into LLMs, vector databases, RAG, LangGraph, CrewAI and MCP agents, FastAPI and Docker deployment, MLOps, AI security and LoRA fine-tuning — closing on an enterprise-grade AI SaaS capstone.
Key Highlights :
- Duration: 9 Months
- Modules: 36 Modules
- Eligibility: 12th Pass Onward
- Includes: Enterprise Capstone
Course Overview
This is the AI Mastery track: nine months, 36 modules, four modules a month, arranged so that each one is usable before the next arrives. It is deliberately built as a single continuous path rather than a set of short courses stapled together — you learn to handle data before you model it, to model before you reach for a language model, and to deploy before you are asked to secure and maintain what you deployed. The final month is given entirely to one enterprise-grade AI SaaS application that you build end to end.
Months one to three are the foundation, and they start further back than most AI courses do. Month 1 is data and programming: advanced Excel and Power Query, Power BI with DAX and business dashboards, then Python from fundamentals through VS Code, the uv package manager and virtual environments, and on into real engineering practice — object-oriented design, exception handling, logging, type hinting, pytest, Ruff and Black. Month 2 is the developer's toolkit and the data layer: Git, GitHub and Git Flow alongside AI coding tools like GitHub Copilot, Cursor and Windsurf; PostgreSQL with database design, window functions and query optimisation; APIs, JSON, FastAPI basics with JWT authentication and Postman; then Pandas 2.x, NumPy, Polars, DuckDB and PyArrow. Month 3 turns that into data science: cleaning, feature engineering, EDA and interactive visualisation with Plotly and Streamlit, statistics and probability, feature selection and preprocessing, scikit-learn with pipelines and cross-validation, then XGBoost, LightGBM and CatBoost with proper model evaluation and hyperparameter optimisation.
Months four to six are the modern AI core. Month 4 is deep learning and computer vision in PyTorch — tensor operations and neural networks, CNNs and transfer learning with OpenCV, YOLO object detection, OCR, image segmentation and Vision Transformers, then Transformers and the Hugging Face ecosystem of tokenizers and the Model Hub. Month 5 opens the language-model half: tokenization, embeddings, context windows and the attention mechanism; prompt engineering, prompt optimisation, system prompts and structured prompting; the OpenAI, Gemini, Claude and Grok APIs plus Ollama and LiteLLM for local and routed access; then embeddings and vector databases across FAISS, ChromaDB, Pinecone, Qdrant and Milvus with semantic search. Month 6 is where those pieces become systems: RAG architecture with hybrid search, re-ranking, evaluation and guardrails; LangChain and LangGraph with prompt templates, chains and memory; CrewAI, the Model Context Protocol, tool and function calling and structured outputs; then AI agents, multi-agent systems, autonomous workflows and enterprise agent design.
Months seven to nine are what separates a demo from a product. Month 7 is application development and deployment: advanced FastAPI with async programming, background tasks and WebSockets; Streamlit, Gradio and Chainlit interfaces; Docker, Docker Compose, Linux, Nginx and reverse proxying; then AWS, Azure AI, Google Vertex AI, Hugging Face Spaces and serverless AI deployment. Month 8 is the operational discipline — MLflow, DVC, model registries, experiment tracking and model monitoring; GitHub Actions and CI/CD pipelines; AI security covering prompt injection, jailbreak defence, secret management and responsible AI; then fine-tuning with PEFT, LoRA, QLoRA, quantization and knowledge distillation. Month 9 adds multimodal AI — text, image, audio and video, Whisper, vision-language models and speech AI — and enterprise architecture with microservices, event-driven systems, Redis, Celery and Kafka, before the capstone: an AI SaaS built with FastAPI, PostgreSQL, RAG pipelines, AI agents, Docker containerisation and full cloud deployment, delivered with documentation, code review, a managed GitHub repository and industry-standard practice.
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
Complete data pipelines, end to end
From Excel and SQL through Python-based data engineering with Pandas, Polars, DuckDB and PyArrow — the first of the syllabus's seven stated learning outcomes.
- 02
Models you train, tune and evaluate
Scikit-learn pipelines and cross-validation, gradient boosting with XGBoost, LightGBM and CatBoost, then PyTorch CNNs, YOLO detection, OCR and Vision Transformers.
- 03
Production LLM applications
RAG with hybrid search, re-ranking, evaluation and guardrails, built on LangChain, LangGraph, CrewAI and MCP against five different vector databases.
- 04
A deployed, monitored, secured system
FastAPI and Docker on AWS, Azure AI or Vertex AI, with GitHub Actions CI/CD, MLflow tracking, model monitoring and prompt-injection defence in place.
Course Curriculum
36 modules across nine months, four modules a month. Months 1–3 build the data and modelling foundation, months 4–6 cover deep learning and the whole language-model stack from tokenization through agents, and months 7–9 take it into production with deployment, MLOps, security, fine-tuning and the enterprise capstone.
Month 1 — Data & Programming Foundations
- 1
Module 1 · Excel & Data Literacy — Excel Advanced, Power Query, AI productivity tooling, data literacy
- 2
Module 2 · Power BI & Reporting — Power BI, DAX, business dashboards, KPI reporting
- 3
Module 3 · Python Fundamentals — Python fundamentals, VS Code, the uv package manager, virtual environments
- 4
Module 4 · Python Engineering Practices — object-oriented programming, exception handling, logging, type hinting, pytest, Ruff, Black
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.
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Who can do this course
Students straight out of 12th
Any stream. Month 1 begins at spreadsheets and Python basics, and the sequencing means you are never asked to use a tool you have not already been taught to build with.
Anyone who wants the full path, not a slice
Short AI courses tend to start at prompt engineering and stop at a chatbot. This one covers the data layer beneath it and the deployment, monitoring and security above it — which is where most projects actually fail.
Students aiming at LLM and agent work
Months 5 and 6 are given entirely to language models: tokenization and attention, prompting, four model APIs plus local inference, five vector databases, RAG with re-ranking and guardrails, LangGraph, CrewAI and MCP.
Students who want to ship, not just train
Docker, Nginx, three cloud AI platforms, CI/CD with GitHub Actions, MLflow experiment tracking and model monitoring. The capstone is deployed, not demoed on a laptop.
Graduates and career changers
The engineering practice in Month 1 — OOP, logging, type hinting, pytest, Ruff — is the part that makes a portfolio readable to an interviewer, and it is taught early rather than assumed.
Anyone building a GitHub portfolio
Module 36 is explicitly about delivery: project documentation, code review practice, repository management and industry standards, alongside resume and portfolio guidance.
Why this programme is worth your year
36 modules, four a month, in dependency order
Data before models, models before language models, deployment before operations. Nothing is used before it has been built.
The data layer taught properly
PostgreSQL with window functions and query optimisation, Pandas 2.x, NumPy, Polars, DuckDB and PyArrow — the pipelines everything downstream depends on.
Five vector databases, four model APIs
FAISS, ChromaDB, Pinecone, Qdrant and Milvus; OpenAI, Gemini, Claude and Grok, plus Ollama and LiteLLM for local and routed inference.
Agents as an engineering discipline
LangGraph state machines, CrewAI crews, MCP tool calling, structured outputs, multi-agent systems and enterprise agent design — not prompt tricks.
Deployment, MLOps and security
Docker and Nginx, AWS, Azure AI and Vertex AI, GitHub Actions CI/CD, MLflow and DVC, model monitoring, prompt injection and jailbreak defence.
One capstone you can defend
An AI SaaS with FastAPI, PostgreSQL, RAG and agents, containerised and deployed — documented, code-reviewed and shipped from a managed GitHub repository.
From Spreadsheets to a Deployed AI SaaS
- 36 modules across nine months — data engineering, machine learning, deep learning, LLMs, RAG, agents, deployment, MLOps and fine-tuning.
- One enterprise-grade capstone: an AI SaaS with FastAPI, PostgreSQL, RAG pipelines, AI agents, Docker and full cloud deployment.
Get certified in Artificial Intelligence Diploma 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 Diploma 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 Diploma 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 syllabus states seven learning outcomes. Build complete data pipelines from Excel and SQL through Python-based data engineering. Train, tune and evaluate machine learning and deep learning models for real tasks. Design and query vector databases for semantic search and RAG systems. Build production LLM applications using LangChain, LangGraph, CrewAI and MCP. Deploy AI applications with FastAPI, Docker, CI/CD and the major cloud AI platforms. Apply AI security, MLOps and responsible AI practices in production systems. And ship a full enterprise-grade AI SaaS capstone with a professional GitHub portfolio behind it.
The nine the syllabus names: Data Analyst, Data Scientist, Machine Learning Engineer, Deep Learning Engineer, LLM / AI Engineer, AI Agent Developer, MLOps Engineer, AI Application Developer and Freelance AI Consultant. The syllabus publishes no salary ranges for them, so none are quoted here — pay at this level varies widely by city, company and, more than anything else, by what your portfolio actually demonstrates.
A Course Completion Certificate, guided support building your resume and portfolio, a deployed enterprise AI SaaS capstone, and the GitHub repository behind it — documented, code-reviewed and managed to the standards taught in Module 36. The work from earlier months sits alongside it: dashboards, pipelines, trained models, a vision system, a RAG application and an agent system.
One end-to-end enterprise-grade AI SaaS application, built across the whole of month nine. FastAPI and PostgreSQL underneath, RAG pipelines and AI agents in the application layer, Docker containerisation and full cloud deployment around it. Module 36 then covers the delivery: project documentation, code review best practices, GitHub repository management, AI project deployment and industry standards.
Because the failures in real AI projects are usually not modelling failures. They are data failures, deployment failures and monitoring failures. Starting at data literacy, Power Query and dashboards means you can read and shape data before you model it — and the modules build in dependency order from there, so nothing is used before it has been taught.
No. The syllabus states a Course Completion Certificate and resume and portfolio guidance; it does not include a vendor certification stack. If you go on to sit exams such as the AWS, Azure or Google Cloud AI credentials, those are attempted on the provider's own platform, at the provider's own fee, and techcadd is not affiliated with or an authorised testing centre for any of those organisations.
Hands-on projects you will ship
Dashboards and reporting
Business dashboards and KPI reporting built in Power BI with DAX, on data shaped in Excel and Power Query — the first thing you can show anyone, built in month one.
A queried database and a working API
A PostgreSQL schema you designed, queried with window functions and tuned for performance, served through a FastAPI endpoint with JWT authentication and tested in Postman.
Trained and tuned models
A full modelling cycle: cleaning and feature engineering, EDA with Plotly and Streamlit, scikit-learn pipelines with cross-validation, then gradient boosting with hyperparameter optimisation and honest evaluation.
A computer vision system
PyTorch neural networks, a CNN adapted by transfer learning, and a detection pipeline using YOLO, OCR and image segmentation with OpenCV — plus Hugging Face transformers and tokenizers.
A semantic search and RAG application
Embeddings indexed in a vector database, then a full RAG system with hybrid search, re-ranking, evaluation and guardrails, orchestrated through LangChain and LangGraph with memory.
An agent system with tools
Tool and function calling with structured outputs, CrewAI crews and MCP integrations, built up into multi-agent autonomous workflows on an enterprise agent design.
A deployed, monitored service
An async FastAPI service with a Streamlit, Gradio or Chainlit interface, containerised with Docker Compose behind Nginx, deployed to AWS, Azure AI or Vertex AI, with GitHub Actions CI/CD, MLflow tracking and model monitoring.
Enterprise Capstone — a complete AI SaaS
The programme's single formal project, and the whole of month nine: an end-to-end enterprise-grade AI SaaS application with FastAPI and PostgreSQL, RAG pipelines and AI agents, Docker containerisation and full cloud deployment — delivered with project documentation, code review, a managed GitHub repository and industry-standard practice.
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.
Understand
Break a real requirement into a clear plan and the right tools.
Dashboards and reportingBuild
Work hands-on with trainer feedback while the decisions are still easy to change.
A queried database and a working APIPresent
Turn the finished work into a portfolio story you can defend in an interview.
Trained and tuned modelsWhy students choose techcadd
techcadd is an ISO 9001 certified institute with campuses in Jalandhar, Ludhiana, Hoshiarpur, Phagwara and Chandigarh. What makes this track work is its ordering: nine months is long enough to teach the foundations honestly instead of skipping them, and the last month exists to turn everything before it into one thing you can show.
Beginners start at month one, not month four
Excel, Power Query, Power BI and Python fundamentals come first. A school leaver with no coding background is the intended starting point, not an exception the course tolerates.
Engineering practice from the start
OOP, exception handling, logging, type hinting, pytest, Ruff and Black in Module 4 — so the code you write in month seven is code someone else can read.
Modern tooling, taught as tooling
uv for environments, GitHub Copilot, Cursor and Windsurf for AI-assisted coding, Postman for APIs. You use the tools the job uses, and you learn what they are doing for you.
Projects at every stage, not only at the end
Dashboards in month one, pipelines in month two, trained models in month three, a vision system in month four, a RAG application in month six, a deployed service in month seven.
Delivery standards are a module
Module 36 covers project documentation, code review best practice, GitHub repository management and industry standards — the part of a portfolio that gets it taken seriously.
Resume and portfolio guidance
Guided support building your resume and portfolio, on top of the Course Completion Certificate. Placement support is support, not a job guarantee.
What our students say
Real experiences from learners across Jalandhar and the districts around it.
“The RAG module went past 'chunk and embed' into re-ranking, evaluation and guardrails. That is what I got asked about, and that is what I could answer.”
KSKaranveer SidhuLLM / AI Engineer · Jalandhar “Three months on data before any modelling felt slow at the time. It stopped feeling slow the first time I was handed a messy dataset in an interview task.”
IBIshita BansalMachine Learning Engineer · Ludhiana “Docker, Nginx, GitHub Actions and a real cloud deployment. My capstone had a URL, not a screenshot, and that changed how people read my CV.”
GSGurnoor SethiAI Application Developer · Phagwara “The RAG module went past 'chunk and embed' into re-ranking, evaluation and guardrails. That is what I got asked about, and that is what I could answer.”
KSKaranveer SidhuLLM / AI Engineer · Jalandhar “Three months on data before any modelling felt slow at the time. It stopped feeling slow the first time I was handed a messy dataset in an interview task.”
IBIshita BansalMachine Learning Engineer · Ludhiana “Docker, Nginx, GitHub Actions and a real cloud deployment. My capstone had a URL, not a screenshot, and that changed how people read my CV.”
GSGurnoor SethiAI Application Developer · Phagwara
“Polars and DuckDB were things I had never heard of and now use daily. The tooling in this course is genuinely current, not five years behind.”
TATanvi AroraData Scientist · Hoshiarpur “LangGraph and MCP were the modules that got me hired. Very few freshers can talk about agent state and tool calling as engineering rather than prompting.”
RKRohan KaithAI Agent Developer · Kapurthala “I came in from a commerce background with no coding at all. Starting at Excel and Python basics is the only reason I kept up.”
SWSimran WaliaAfter 12th Student · Nakodar “Polars and DuckDB were things I had never heard of and now use daily. The tooling in this course is genuinely current, not five years behind.”
TATanvi AroraData Scientist · Hoshiarpur “LangGraph and MCP were the modules that got me hired. Very few freshers can talk about agent state and tool calling as engineering rather than prompting.”
RKRohan KaithAI Agent Developer · Kapurthala “I came in from a commerce background with no coding at all. Starting at Excel and Python basics is the only reason I kept up.”
SWSimran WaliaAfter 12th Student · Nakodar
Frequently asked questions
Find answers to the questions students ask before enrolling.
Nine months, 36 modules, four modules a month. Months 1–3 cover data and programming foundations, dev tools and data engineering, then data science and machine learning. Months 4–6 cover deep learning and computer vision, LLM fundamentals and vector search, then RAG, LangChain and AI agents. Months 7–9 cover application development and deployment, MLOps, security and fine-tuning, then multimodal AI and the enterprise capstone.
No specific stream is required. Statistics and probability are taught in Module 10, at the point they are needed for preprocessing and feature selection, rather than assumed at the door.
Length is what buys depth here. The nine-month path adds the full data-engineering foundation, deep learning and computer vision with YOLO, OCR and Vision Transformers, the complete deployment and MLOps stack, AI security and fine-tuning with LoRA and QLoRA, multimodal AI and enterprise architecture — and it dedicates an entire final month to a single enterprise capstone rather than a shorter project. Speak to a counsellor about which fits your timeline.
Python, Excel, Power BI and DAX, PostgreSQL, Pandas, NumPy, Polars, DuckDB and PyArrow; VS Code, Git, GitHub, Copilot, Cursor, Windsurf, pytest, Ruff, Black and Postman; scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, OpenCV, YOLO and Hugging Face; the OpenAI, Gemini, Claude and Grok APIs plus Ollama and LiteLLM; LangChain, LangGraph, CrewAI and MCP; FAISS, ChromaDB, Pinecone, Qdrant and Milvus; FastAPI, Streamlit, Gradio and Chainlit; Docker, Nginx, AWS, Azure AI, Google Vertex AI and Hugging Face Spaces; MLflow, DVC, GitHub Actions, Redis, Celery and Kafka.
Both, in that order. Prompt engineering, prompt optimisation, system prompts and structured prompting come in Module 18. Fine-tuning comes in Module 32 once you have shipped applications: LLM fine-tuning concepts, PEFT, LoRA, QLoRA, quantization and knowledge distillation — including when adapting a model is the right call and when it is not.
Yes, as a full module. Module 31 covers AI security, prompt injection, jailbreak defence, secret management and responsible AI, and it sits alongside experiment tracking, CI/CD and model monitoring in month eight — treated as production engineering rather than as a policy afterthought.
Taught. Module 5 covers GitHub Copilot, Cursor AI and the Windsurf IDE alongside Git, GitHub and Git Flow. They are part of how the work is done now — the point of the engineering practice in Module 4 is that you can read, test and correct what they produce.
The syllabus designates one formal project — the month-nine enterprise capstone. But work is produced throughout: dashboards in month one, a database and an API in month two, trained and tuned models in month three, a vision system in month four, a semantic search and RAG application in months five and six, an agent system in month six, and a containerised, cloud-deployed service in months seven and eight.
A Course Completion Certificate recognising successful completion of the programme, plus guided support building your resume and portfolio. The syllabus does not include vendor certification exams — those are separate, attempted on the provider's own platform at the provider's own fee.
A workstation is provided. A personal laptop is strongly recommended for capstone work and practice outside class hours, particularly during the deployment months.
No. techcadd provides placement support — resume and portfolio guidance, profile circulation and interview preparation — but final selection depends on your performance and, at this level, on the capstone and GitHub repository you can show. The institute does not sell guaranteed-job promises.
Speak to a counsellor first — walk in at the Jalandhar campus or call, and confirm this nine-month track is the right length for your goals against the shorter AI programmes. Registration takes ID and qualification proof and photographs, with instalment options on fees, followed by batch allocation and orientation.
Ask about Artificial Intelligence Diploma 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 Diploma 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.