AI & Machine Learning
Python, real datasets and models you train yourself — plus the judgement to know when a model is lying to you. Built for people who need this on a résumé, not a certificate wall.
About the program
Most AI courses stop at vocabulary. This one starts with a dataset that is messy, incomplete and slightly wrong — because that is what every real one looks like — and does not finish until the learner has cleaned it, modelled it, and explained honestly what the model can and cannot be trusted with.
The path is Python first, then pandas and NumPy for data, then scikit-learn for the models: regression, classification, clustering, and the evaluation that separates a working model from a lucky one. Along the way learners use modern AI tools properly — prompting, retrieval and automation — rather than treating them as magic.
It ends with a portfolio project on data the learner chooses. That project, not the certificate, is what gets discussed in interviews.
Who is it for
- College students in any stream, including non-engineering
- Working professionals moving into data or AI roles
- Teachers preparing to deliver the CBSE AI skill subject
- Anyone who wants AI on their résumé and can defend it
What you'll gain
- Working Python for data, not just syntax
- pandas, NumPy and clean-data discipline
- Regression, classification and clustering, hands on
- Honest model evaluation, bias and failure modes
- Confident, practical use of modern AI tools
- A portfolio project on a dataset you chose
The course, module by module
Every module ends with something the student has actually built, not just notes in a book.
Python for data
Enough Python to be dangerous with data, taught fast for people who have coded a little or not at all.
Data handling
pandas and NumPy: loading, cleaning, joining and reshaping data that arrives broken.
Exploring data
Distributions, correlations and charts — finding the story before choosing a model.
Regression
Predicting numbers, and understanding what the coefficients are actually claiming.
Classification
Decision trees and beyond, plus precision, recall and why accuracy alone misleads.
Clustering
Finding groups nobody labelled, and judging whether the groups mean anything.
AI tools in practice
Prompting, retrieval and automation used deliberately, with their limits made explicit.
Portfolio project
Your dataset, your model, your write-up — reviewed and defended.
Get program details & fees
Fill the form and our counsellor reaches out with the full plan, schedule and fees for AI & Machine Learning.