Learn AI. Build Projects. Create Your Future.

Module 1: Python Fundamentals

Variables · Data Types · Input/Output · Operators · Type Conversion

Module 2: Control Flow

if/elif/else · for loops · while loops · break · continue · nested loops

Module 3: Data Structures

Lists · Tuples · Sets · Dictionaries

Module 4: Functions

Parameters · Return values · Lambda · Scope

Module 5: Object-Oriented Programming

Classes · Objects · Constructors · Inheritance · Polymorphism

Module 6: Files and Exceptions

File handling · CSV · Exceptions

Module 7: Python Libraries

NumPy · Pandas · Matplotlib

Module 8: Projects

Guided builds · Independent mini project

Module 1: Introduction to Generative AI

What GenAI can and cannot do

Module 2: How Large Language Models Work

Tokens, context, limits

Module 3: Prompt Engineering

Briefs, constraints, iteration

Module 4: ChatGPT and AI Assistants

Everyday study and work tasks

Module 5: AI Image Generation

Visual briefs and review

Module 6: AI for Research

Sources, notes, checking claims

Module 7: AI for Presentations

Outlines, slides, speaker notes

Module 8: AI for Productivity

Drafting, summarising, planning

Module 9: AI Automation

Repeatable workflows

Module 10: Build GenAI Applications

A small assistant you can demo

Module 1: Introduction to Machine Learning

Problems ML can solve

Module 2: Data Preparation

Cleaning, splits, leakage

Module 3: Supervised Learning

Prediction vs classification

Module 4: Linear Regression

Fit, interpret, diagnose

Module 5: Logistic Regression

Classification basics

Module 6: KNN

Distance-based models

Module 7: Decision Trees

Splits and overfitting

Module 8: Random Forest

Ensembles

Module 9: XGBoost

Boosting in practice

Module 10: Unsupervised Learning

When there is no label

Module 11: Clustering

Grouping patterns

Module 12: PCA

Dimensionality reduction

Module 13: Model Evaluation

Honest metrics

Module 14: Feature Engineering

Useful inputs

Module 15: End-to-End ML Projects

From data to demo