Best AI, Machine Learning & Generative AI Course in Jalandhar
techcadd’s AI, Machine Learning & Generative AI Course in Jalandhar is a job-focused training program that takes you from Python basics to building real AI applications. You learn Machine Learning, Deep Learning, LLMs, RAG chatbots and AI agents through hands-on projects, not just theory.
- Complete career track
- 6 MonthsComplete career track
- Plus a capstone
- 11 ModulesPlus a capstone
- On your GitHub
- 5 ProjectsOn your GitHub
- Total fee, 0% EMI
- ₹38k – ₹48kTotal fee, 0% EMI
Your 6-month path
Batches open- 1Python
- 2Machine Learning
- 3Deep Learning
- 4Generative AI
- 5RAG
- 6AI Agents
- 7Deployment
Offline at our Jalandhar centre or live online — same syllabus, same trainers.
- Jupyter Notebook
- VS Code
- Git
- GitHub
- Google Colab
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- SQL
- Scikit-Learn
- XGBoost
- LightGBM
- TensorFlow 2.x
- Keras
- PyTorch
- OpenCV
- YOLOv8
- NLTK
- spaCy
- Hugging Face Transformers
- OpenAI API
- Anthropic Claude API
- LangChain
- LlamaIndex
- LangGraph
- Pinecone
- ChromaDB
- FAISS
- FastAPI
- Streamlit
- Docker
- MLflow
- DVC
- GitHub Actions
Built for people who wantto build AI, not just talk about it
Artificial Intelligence is now used in banking, healthcare, e-commerce, manufacturing, marketing and customer support. Companies are looking for people who can actually build and apply AI, not just talk about it. This course is designed for exactly that: every concept is taught with code, every module ends with practical work, and the final result is a portfolio you can show to employers or clients.
Start your AI journey from zero with no prior coding or AI knowledge required. Learn everything from Python and Machine Learning to Deep Learning, Generative AI, RAG and AI Agents in one complete program, with no separate paid add-ons. Choose offline classes at our Jalandhar centre or live online learning from anywhere in India, with portfolio building, GitHub, resume and interview support to prepare you for an AI career.
Students & freshers
An in-demand skill before you start applying for jobs.
Non-IT graduates
A structured way to switch into tech from any stream.
Working professionals
A route into AI, data or automation roles.
Developers
Machine Learning and Generative AI added to your stack.
Freelancers
AI tools you can build and sell to clients.
Business owners
Automate your own work instead of outsourcing it.
Program overview& quick facts
Searching for an AI certificate course in Jalandhar? Most students want clear answers before they enrol: how long the course is, who can join, what it costs, which tools they will learn and whether the training is practical. Here they are in one place, so you don’t have to call to ask.
Course duration
6 Months
A comprehensive career track, start to finish.
Batches
Weekday · Evening · Weekend
Pick the timing that fits your college or job.
Eligibility
B.Tech, BCA, BSc, MCA, Graduates
Non-CS learners welcome through our Module 0 onboarding.
Training mode
100% Practical Labs
In-person at the Jalandhar centre, with 1-on-1 mentorship slots.
Core tech stack
Python · PyTorch · LangChain
Plus LangGraph, Pinecone, FastAPI and AWS SageMaker.
Course fees
₹38,000 – ₹48,000
Transparent total pricing with 0% interest EMI options.
The AI training standard:how we compare
Ten things worth checking at any AI institute in Jalandhar — what you get at techcadd, and why each one matters.
| What to check | What you get at techcadd | Why it matters |
|---|---|---|
| Generative AI & RAG | Included in the main course: LLMs, prompt engineering, RAG chatbots and AI agents | Generative AI is what employers ask for today, and it shouldn’t need a separate paid add-on |
| Hands-on practice | Daily practical sessions on real datasets, assignments and projects | AI is learned by building models, not by watching slides |
| Fee transparency | Fee range listed upfront: ₹38,000 to ₹48,000, with 0% interest EMI | You should know the total cost before you walk in or fill a form |
| Maths & statistics | Statistics, probability and linear algebra taught in simple, practical steps with code | Understanding the basics helps you debug models and clear interviews |
| Syllabus currency | Updated for 2026 with current tools and frameworks | Outdated syllabi leave you with skills the market no longer asks for |
| Certification | techcadd completion certificate | Certificates support your resume, but your projects carry the real weight |
| Projects & portfolio | Industry-style projects and a capstone, documented on GitHub | Recruiters and clients look at what you have built |
| Career support | Resume building, mock interviews and interview preparation | Skills need to be presented well to turn into offers |
| Learning mode | Jalandhar classroom plus live online classes for students across India | You can learn in the way that fits your location and schedule |
| Computing | Machine Learning on any laptop; Deep Learning and LLM work needs more power, so ask us where you will train | Deep Learning and LLM work needs enough computing power to practise properly |
Generative AI & RAG
Included in the main course: LLMs, prompt engineering, RAG chatbots and AI agents
Generative AI is what employers ask for today, and it shouldn’t need a separate paid add-on
Hands-on practice
Daily practical sessions on real datasets, assignments and projects
AI is learned by building models, not by watching slides
Fee transparency
Fee range listed upfront: ₹38,000 to ₹48,000, with 0% interest EMI
You should know the total cost before you walk in or fill a form
Maths & statistics
Statistics, probability and linear algebra taught in simple, practical steps with code
Understanding the basics helps you debug models and clear interviews
Syllabus currency
Updated for 2026 with current tools and frameworks
Outdated syllabi leave you with skills the market no longer asks for
Certification
techcadd completion certificate
Certificates support your resume, but your projects carry the real weight
Projects & portfolio
Industry-style projects and a capstone, documented on GitHub
Recruiters and clients look at what you have built
Career support
Resume building, mock interviews and interview preparation
Skills need to be presented well to turn into offers
Learning mode
Jalandhar classroom plus live online classes for students across India
You can learn in the way that fits your location and schedule
Computing
Machine Learning on any laptop; Deep Learning and LLM work needs more power, so ask us where you will train
Deep Learning and LLM work needs enough computing power to practise properly
Why eachpoint matters
- 01
Generative AI should be part of the course
Many programs teach only classical Machine Learning in the base course and add Generative AI later at extra cost. At techcadd, LLMs, RAG and AI agents are covered within the course, so you finish with current, job-relevant skills.
- 02
Practical training decides your skill level
A good AI course has you writing code in every session, working on real data and getting feedback from the trainer. Ask how much is coding and how much is theory.
- 03
Computing access affects what you can practise
Basic Machine Learning runs on any laptop; Deep Learning and LLM projects need more power. Ask where you will train models and whether there are usage limits.
- 04
Fees should be clear before you enrol
You should find the total fee, what it covers and the payment options without filling a form or waiting for a callback — and know of any extra charges later.
- 05
Maths and statistics, made simple
You don’t need to be a maths expert. A good course teaches probability, statistics, linear algebra and gradient descent in plain language, alongside code.
- 06
The syllabus should match today’s industry
Check for LLM APIs, vector databases, LangChain and deployment with FastAPI or Streamlit — not just older algorithms.
- 07
Proof of skill matters more than certificates
Employers look at your GitHub, your projects and how well you explain your work in interviews. Choose a course that invests in that.
- 08
Career support should be specific
Look for resume review, portfolio guidance, mock interviews and interview practice. Be careful of vague promises such as “100% placement”.
Questions to ask any AI institute before you enrol
- 1Is Generative AI included in the course fee and the course duration?
- 2What are the total fees, and what do they include?
- 3How many projects will I build, and can I see examples?
- 4Who will teach me, and what is their industry experience?
- 5Can I attend a free demo class first?
- 6Where will I practise, and what computing resources are available?
- 7What happens if I miss a class or need extra help?
Ask them at techcadd too.
Book a free demo class, meet the trainers, see the labs and the syllabus, and then decide — with every one of these questions answered.
Core toolchain &technologies covered
From the everyday tools every AI developer uses to the production practices large companies and MNCs expect. Each one is used in practical sessions and projects, so you can list it on your resume with real work behind it.
Core environments
- Jupyter Notebook
- VS Code
- Git
- GitHub
- Google Colab
Write and test code, track versions and collaborate the way real dev teams do
Data processing
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- SQL
Clean, analyse and visualise data before any model is built
Machine Learning
- Scikit-Learn
- XGBoost
- LightGBM
Build prediction, classification and clustering models and tune them
Deep Learning
- TensorFlow 2.x
- Keras
- PyTorch
Train neural networks, CNNs, RNNs/LSTMs and Transformers
Computer Vision
- OpenCV
- YOLOv8
Detect objects, track movement and build image-based systems
NLP
- NLTK
- spaCy
- Hugging Face Transformers
Build sentiment analysis, text classification and language models
Generative AI
- OpenAI API
- Anthropic Claude API
- LangChain
- LlamaIndex
Build chatbots, text tools and AI-powered apps
AI Agents
- LangGraph
- Tool calling
Build multi-step agents that plan, use tools and complete tasks
Vector databases
- Pinecone
- ChromaDB
- FAISS
Store embeddings and power semantic search and RAG
Deployment
- FastAPI
- Streamlit
- Docker
Turn a model into a live API or web app
MLOps
- MLflow
- DVC
- GitHub Actions
Track experiments, version data and models, automate testing and release
How these tools come together
Collect and clean data with Python and SQL, train and evaluate a model with Scikit-Learn or PyTorch, track experiments with MLflow, wrap it in a FastAPI service, package it with Docker and deploy it behind a Streamlit front end. For Generative AI the same workflow extends to embeddings, a vector database, a RAG pipeline and a LangGraph agent.
Advanced concepts used inMNCs and product companies
Beyond the tools, large organisations expect you to understand how AI systems are built, tested and maintained at scale. These topics separate a course project from production-level work.
- 01
Fine-tuning LLMs
Adapt open-source models to your own data with LoRA and QLoRA — and know when RAG or prompting is enough.
- 02
Advanced RAG
Chunking strategies, hybrid search, re-ranking, metadata filtering and large document collections.
- 03
Evaluation & guardrails
Measure answer quality, detect hallucinations, add safety checks and build test sets.
- 04
Experiment tracking
Log experiments, compare model versions and reproduce results with MLflow.
- 05
Docker containers
Package model and code so it runs the same on every machine.
- 06
CI/CD for ML
Automate testing, building and deployment with GitHub Actions.
- 07
Monitoring & drift
Watch predictions, detect data and concept drift and decide when to retrain.
- 08
Speed & cost
Quantisation, batching, caching, ONNX export and token-cost control.
- 09
Cloud deployment
Scalable APIs, endpoints, storage and basic cost management on AWS.
- 10
Data pipelines
How data moves from source to model through pipelines and schedulers.
- 11
Responsible & secure AI
Bias, data privacy, prompt-injection risks and safe handling of company data.
- 12
Professional teamwork
Git branching, pull requests, code review and documentation that reads like real engineering.
Complete module-by-moduleAI syllabus
This syllabus takes you from Python basics to deploying production-style AI systems in 6 months, in the order that makes learning easiest. Each module lists what you learn, what you practise and what you can do by the end of it.
- 1
Months 1–2
Data Foundations & Classical Logic
- 2
Months 3–4
Deep Learning & Architectural Frameworks
- 3
Months 5–6
Production-Grade Generative AI & MLOps
Phase 1 · Months 1–2 · 4 modules
Data Foundations & Classical Logic
Become comfortable with Python and data, and build and evaluate your first Machine Learning models.
An end-to-end Machine Learning project, from raw data to a tuned, explained model, documented on GitHub.
Advanced Python for Data Science
- What you learn
- Variables, data types, lists, dictionaries, loops, functions, file handling, OOP basics, error handling, NumPy and data cleaning with Pandas.
- Advanced topics
- Clean reusable code, list comprehensions, virtual environments, APIs and JSON, Git/GitHub basics.
- Hands-on practice
- Clean a messy real-world dataset — missing values, duplicates, outliers, wrong types — and summarise it.
Load, clean, transform and explore any tabular dataset using Python.
Mathematical Engine Essentials
- What you learn
- Vectors and matrices, descriptive statistics, probability, distributions, hypothesis testing and gradient descent.
- Advanced topics
- Bias-variance trade-off, correlation vs causation, data leakage, scaling and normalisation.
- Hands-on practice
- Code gradient descent from scratch and visualise how a model learns step by step.
Understand what is happening inside a model, not just call a library function.
Supervised & Predictive Modelling
- What you learn
- Linear and logistic regression, k-NN, decision trees, random forests, SVM and XGBoost / LightGBM.
- Advanced topics
- Feature engineering, encoding, SMOTE and class weights, GridSearch / RandomSearch, Scikit-Learn pipelines.
- Hands-on practice
- Build a churn or sales prediction model with a proper train/validation/test workflow.
Choose, train and tune the right model for a business problem.
Unsupervised Frameworks & Metrics
- What you learn
- K-Means and hierarchical clustering, PCA, anomaly detection and recommendation basics; accuracy, precision, recall, F1, ROC-AUC and k-fold validation.
- Advanced topics
- Choosing the right metric for the business goal, SHAP explainability, avoiding overfitting.
- Hands-on practice
- Segment customers with clustering and explain predictions to a non-technical audience.
Evaluate models honestly and explain why they make a given prediction.
Phase 2 · Months 3–4 · 3 modules
Deep Learning & Architectural Frameworks
Learn how neural networks work and apply them to images and text.
One Computer Vision project and one NLP project, each with a documented report and demo.
Neural Network Fundamentals
- What you learn
- Neurons, layers, activation and loss functions, backpropagation, SGD and Adam, PyTorch with a Keras/TensorFlow overview.
- Advanced topics
- Dropout, batch normalisation, learning-rate scheduling, early stopping, GPU basics, MLflow tracking.
- Hands-on practice
- Build a neural network from scratch, then rebuild it in PyTorch and compare results.
Design, train and debug a network and know why it is or isn’t learning.
Computer Vision & Spatial Streams
- What you learn
- OpenCV, CNNs, transfer learning, image classification, YOLOv8 object detection and segmentation basics.
- Advanced topics
- Data augmentation, mAP and IoU, real-time video, model size vs speed trade-offs.
- Hands-on practice
- Build an object or defect detection system for images and live video.
Create and deploy vision models for inspection, attendance or security.
Classical NLP & Sequence Analytics
- What you learn
- Text cleaning, tokenisation, TF-IDF, Word2Vec, sentiment analysis, RNN / LSTM / GRU and Transformers with BERT.
- Advanced topics
- Attention, Hugging Face pretrained models, fine-tuning on your own data, named entity recognition.
- Hands-on practice
- Build a sentiment or support-ticket classifier with a fine-tuned BERT model.
Process text and fine-tune language models for specific tasks.
Phase 3 · Months 5–6 · 4 modules
Production-Grade Generative AI & MLOps
Build Generative AI applications and deploy them the way companies do.
An end-to-end AI application with a README, architecture diagram and demo video — plus resume, LinkedIn, GitHub review, mock interviews and a final presentation to trainers and peers.
Enterprise Generative AI & LLM Systems
- What you learn
- Tokens, context windows, temperature, prompt engineering, structured outputs, tool calling and LLM APIs (OpenAI, Anthropic Claude and others).
- Advanced topics
- Prompt testing, token-cost control and caching, streaming, rate limits, intro to LoRA / QLoRA.
- Hands-on practice
- Build an AI assistant with memory, structured responses and cost tracking.
Use LLMs reliably and affordably inside real applications.
RAG Pipelines & Semantic Vector Spaces
- What you learn
- Embeddings, PDF/web/CSV loading, chunking, Chroma, Pinecone and FAISS, RAG with LangChain / LlamaIndex.
- Advanced topics
- Hybrid search, re-ranking, metadata filtering, source citations, evaluating RAG quality.
- Hands-on practice
- Build a chatbot that answers from policy documents and shows each source.
Build the enterprise knowledge assistant businesses ask for.
Multi-Agent AI Architecture
- What you learn
- What agents are, tool calling, planning and reasoning loops, memory, agents with LangGraph.
- Advanced topics
- Planner–researcher–reviewer workflows, human-in-the-loop approval, error recovery, guardrails, prompt-injection safety.
- Hands-on practice
- Build an agent that uses search, database or API tools and returns a checked result.
Design controlled, multi-step AI workflows instead of single prompts.
Production MLOps & System Launch
- What you learn
- FastAPI serving, Streamlit front ends, Docker and cloud deployment.
- Advanced topics
- CI/CD with GitHub Actions, MLflow and DVC, monitoring and drift, logging, security and cost.
- Hands-on practice
- Package a model or RAG app as an API, containerise it and deploy a demo link.
Take an AI project from notebook to a live, monitored application.
What you will have at the end
- A solid Python and data foundation
- ML, Deep Learning, Computer Vision and NLP projects
- Generative AI, RAG and AI agent applications
- A deployed project with an API and front end
- A GitHub portfolio, a resume and interview practice
Production-gradeportfolio projects
Recruiters hire people who can show working projects, not just certificates. Each project solves a business problem and goes on your GitHub with a demo and a short write-up.
Project 1
Sales Forecasting System
- Problem it solves
- Businesses need to predict future sales to plan stock, staff and budgets.
- What you build
- A Machine Learning model that forecasts demand from regional and seasonal sales data.
- Final output
- A forecasting app with charts and accuracy metrics (MAE, RMSE).
- Python
- Pandas
- Scikit-Learn
- XGBoost
- Streamlit
Useful for: Data Scientist · Data Analyst · ML Engineer
Project 2
RAG Chatbot for Company Documents
- Problem it solves
- Employees waste time searching long policy PDFs for simple answers.
- What you build
- A chatbot that reads multiple documents, finds the right section through vector search and answers with the source shown.
- Final output
- A chat interface that gives cited answers from uploaded documents.
- LLM APIs
- Embeddings
- Chroma / Pinecone
- LangChain
Useful for: Generative AI Developer · AI Engineer
Project 3
Real-Time Defect Detection
- Problem it solves
- Manual quality checks are slow and inconsistent in factories and workshops.
- What you build
- A computer vision system that spots faulty products in images and live camera video.
- Final output
- A live demo that marks defective items in real time, with a performance report.
- OpenCV
- YOLOv8
- PyTorch
- mAP
Useful for: Computer Vision Engineer · AI Engineer
Project 4
Model Deployment with FastAPI
- Problem it solves
- A model stuck in a notebook has no business value.
- What you build
- A live API and front end for one of your trained models, packaged and deployed.
- Final output
- A live link that accepts input and returns predictions.
- FastAPI
- Docker
- GitHub Actions
- Streamlit
Useful for: ML Engineer · MLOps · Backend / AI Engineer
Final weeks
Capstone Project
One end-to-end AI application of your choice, built with trainer guidance: a clear problem, data or documents, a trained model or LLM workflow, an API, a front end and a deployment link.
- GitHub repo
- README
- Architecture diagram
- Demo video
How these projects help you get hired
- Recruiters see working code on your GitHub, not just a certificate.
- You get real examples to explain in interviews.
- They cover what employers ask for most: ML, Computer Vision, Generative AI and deployment.
- Each project becomes a strong resume and LinkedIn entry.
Who is this programdesigned for?
Anyone who wants to build a career or business skill in AI. You don’t need prior coding or AI experience: the course starts from Python basics and builds up step by step, so you are never expected to know something before it is taught.
Final-year students & freshers
B.Tech, BCA, MCA, BSc IT, BCom, BA and diploma students or recent graduates — including students from LPU, DAV University, CT University, HMV, Doaba College and KMV.
- 4+ portfolio projects and a capstone
- A GitHub profile with documented work
- Resume, LinkedIn and mock interviews
Best time to join: final year or the semester before placements.
12th pass & early learners
Students who have finished 12th and want to start building tech skills alongside or before college.
- A structured path from Python to AI projects
- A trainer guiding each step
- No time wasted on random tutorials
Graduate with skills and projects most classmates won’t have.
Working professionals & switchers
Developers, testers, analysts, IT support and professionals from operations, sales, marketing, finance or HR.
- A beginner-friendly start, no coding prerequisite
- Real business use cases
- Evening and weekend batches
Upskill without quitting your job.
Entrepreneurs & freelancers
Business owners, founders, consultants, agency owners and freelancers.
- Chatbots trained on your own FAQs
- Email, invoice and report automation
- Sales and demand forecasting
Build your own tools or take AI projects from clients.
Tech learners upgrading
People who have done basic Python or an online course and want structured, project-based depth.
- A full syllabus with trainer feedback
- Code review on your work
- End-to-end deployment projects
Fill the gaps self-learning leaves in maths, projects and deployment.
Who should think twice
This course is hands-on by design, so it is not the right fit for everyone.
- Only want a short theory overview?
- Can’t make time for regular practice?
- Expecting placement without effort?
Unsure? Attend a free demo class first.
What background do you need?
- Coding experience
- Not required — Python is taught from the basics.
- Maths
- School-level basics; statistics and linear algebra taught in simple steps.
- Degree
- Any stream is welcome.
- Laptop
- Needed for practice between classes.
- Time commitment
- Class time plus regular practice at home.
- Mindset
- Curiosity, consistency and willingness to practise.
Which batch should you choose?
- 1Students & freshers
- Weekday or regular batches, ideally before the final-year placement season.
- 2Working professionals
- Evening or weekend batches that fit around your job.
- 3Outside Jalandhar
- Live online classes with the same syllabus and trainer support.
- 4Not sure yet
- Talk to a counsellor and attend a demo class first.
AI course with placementsupport in Jalandhar
Looking for an AI course in Jalandhar with career support? techcadd helps you move from learning to earning: you build projects, prepare your resume and practise interviews, so you can apply for AI, data and Python roles with confidence.
What we provide
Resume building
An ATS-friendly resume that highlights your AI skills, tools and projects.
LinkedIn & GitHub setup
A profile recruiters can find, with documented projects.
Portfolio guidance
Each project presented with a README, demo and results.
Mock technical interviews
Python, Machine Learning, Deep Learning and Generative AI questions.
Mock HR interviews
Self-introduction, career goals and communication practice.
Job-search guidance
How to use job portals, referrals and direct applications.
Jobs you can apply for
Python Developer
Entry-level roles in software and data teams
Data Analyst
Analysis, reporting and dashboards
ML Engineer (entry level)
Model building and evaluation
Generative AI Developer
LLM apps, RAG chatbots and AI automation
Data Science Trainee
Junior roles and internships
Freelance AI Developer
Chatbots, automation and forecasting for clients
How we prepare you for jobs, step by step
- 1
During the course
Build projects and keep your GitHub updated.
- 2
Final phase
Complete your capstone and finalise your portfolio.
- 3
Profile building
Create your resume, LinkedIn and GitHub presentation.
- 4
Mock interviews
Practise technical and HR rounds and get feedback.
- 5
Job search
Apply with trainer guidance and interview support.
No institute can honestly guarantee a job — your results depend on your skills, projects and effort. Career support helps you present them well.
AI training institute in Jalandhar:how to reach us
Jalandhar campus
- Address
- techcadd, 2nd Floor, Crystal Plaza, SCS 78, Opposite PIMS Hospital, Jalandhar, Punjab 144001
- Easy to reach from
- Easy to reach from Model Town, Urban Estate, BMC Chowk, Jalandhar Cantt and Nakodar.
- Visit before you enrol
- Attend a free demo class, meet the trainers and check the syllabus and fees before you enrol.
Students from Punjab & other states
- Live online classes
- Live online classes with the same syllabus, projects and trainer support, from home.
- Who can join
- Students and professionals from Punjab, Haryana, Himachal Pradesh, Delhi, Jammu & Kashmir and across India.
- Career support online
- Resume review, mock interviews and portfolio guidance online.
Frequently asked questions:AI course in Jalandhar
Take the first step toward your career in AI
Not sure if this course is right for you? Attend a free demo class at techcadd’s Jalandhar campus and decide with clear information.
In the demo class you meet the trainer, watch a live session, review the syllabus and sample projects, and see the classroom and lab setup. You also get clear answers on fees, batches and career support before paying anything. Live outside Jalandhar? Request an online demo.
The demo is useful for
- Students and freshers planning their first tech job
- Working professionals planning a career switch
- Non-IT graduates unsure whether they can learn AI
- Business owners and freelancers using AI in their work