Introduction to Artificial Intelligence

1. Understand the foundations of AI, its applications, and how it’s transforming industries.

Python for AI

1. Learn Python programming — the core language used in AI and machine learning projects.

Data Handling & Preprocessing

1. Explore how to collect, clean, and prepare data for training machine learning models.

Supervised & Unsupervised Learning

1. Learn core ML techniques: classification, regression, clustering, and dimensionality reduction.

Deep Learning with Neural Networks

1. Get hands-on experience with neural networks and frameworks like TensorFlow or PyTorch.

Natural Language Processing (NLP)

1. Understand how machines process and interpret human language for tasks like chatbots and translation.

Computer Vision

1. Learn how to teach machines to analyze and understand images and videos.

Model Evaluation & Optimization

1. Evaluate model performance and improve accuracy through tuning and cross-validation.

Capstone Project

1. Build an end-to-end AI/ML solution for a real-world problem — with mentor support.

Ethics & Future of AI

1. Explore societal impact, bias in algorithms, and ethical challenges in deploying AI systems.