Module 1: Introduction to AI

Learn AI, ML & Deep Learning basics with real-world examples.

Module 2: Python for AI

Python basics, variables & data types, loops, conditionals, functions, lists, tuples, dictionaries, NumPy, Pandas, math for AI

Module 3: Statistics & Math Foundations

Mean, median, mode, probability, combinations, vectors, matrices, statistics basics

Module 4: Statistical Analysis

Variance, normal distribution, correlation, hypothesis testing, confidence interval

Module 5: Machine Learning

Supervised & unsupervised learning, regression, classification, clustering, model evaluation, deep learning basics

Module 6: Deep Learning & NLP

Neural networks, deep learning libraries, backpropagation, loss functions, activation functions, NLP introduction

Module 7: NLP & LLMs

NLP basics, chatbots, sentiment analysis, LLMs, transformers, computer vision overview

Module 8: Computer Vision

Image recognition basics, CNN, image classification, OpenCV basics

Examination Scheme

1. Theory: 30%
2. Practical: 40%
3. Project + Viva: 30%