Topics Covered:

● What is Artificial Intelligence, Machine Learning & Deep Learning

● Real-world AI Applications and Industry Use-Cases

● Introduction to Python for AI

● Numpy, Pandas, and Matplotlib for Data Handling and Visualization

● Key Mathematical Concepts:

● Linear Algebra (Vectors, Matrices, Transformations)

● Statistics & Probability (Distributions, Mean, Variance)

● Calculus Essentials for ML (Derivatives & Gradients)

● Data Collection, Cleaning & Preprocessing

● Mini-Project: Data Cleaning & Visualization Dashboard

Objective: Understand algorithms that power modern AI systems.

Topics Covered:
● Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
● Regression Algorithms: Linear, Polynomial, Ridge, Lasso
● Classification Algorithms: Logistic Regression, K-NN, Naïve Bayes, SVM
● Clustering Techniques: K-Means, Hierarchical, DBSCAN
● Model Evaluation: Confusion Matrix, ROC, Precision-Recall, F1 Score
● Overfitting & Regularization Techniques
● Model Optimization: Grid Search, Hyperparameter Tuning
● Mini-Project: Predictive Model for Sales or Student Performance

Objective: Explore advanced AI models that can generate new content.

Topics Covered:
● What is Generative AI?
● Understanding GANs (Generative Adversarial Networks)
 ○ Generator & Discriminator Concept
 ○ Building Your Own GAN
● Introduction to Large Language Models (LLMs)
 ○ Architecture of GPT, BERT, and Transformer Models
 ○ Prompt Engineering Techniques
 ○ Fine-Tuning Language Models
● Text-to-Image Models: DALL·E, Midjourney, and Stable Diffusion
● Text-to-Code and Text-to-Video Models
● Mini-Projects:
 ○ Create Your Own Chatbot using GPT API
 ○ Build AI Image Generator using Stable Diffusion