AI Foundations & Real-World Applications
Understand the core concepts of Artificial Intelligence, including its history, key applications, and differences between AI, Machine Learning, and Deep Learning. The goal is to build a strong conceptual understanding of how AI is used across industries.

Mathematics & Statistics for AI
Learn the essential mathematics behind AI, including linear algebra, calculus, and probability. The goal is to understand how mathematical concepts support machine learning models and intelligent systems.

Python Programming for AI
Develop programming skills using Python, focusing on variables, loops, functions, and data handling. Work with key libraries like NumPy, Pandas, and Matplotlib to process and visualize data effectively.

Machine Learning Fundamentals
Explore supervised and unsupervised learning, and implement basic algorithms such as regression, classification, and decision trees. The goal is to build and evaluate simple machine learning models using real-world datasets.

Neural Networks & Deep Learning Basics
Understand perceptrons, feedforward neural networks, and activation functions. Gain hands-on experience building and training a simple neural network using frameworks like TensorFlow or PyTorch.

Natural Language Processing (NLP)
Learn how to preprocess text data using tokenization, stemming, and lemmatization, and implement basic NLP tasks such as sentiment analysis. The goal is to analyze and extract insights from textual data.

Ethics & Social Impact of AI
Examine important topics such as bias, fairness, and ethical responsibility in AI development. The goal is to understand the societal impact of AI technologies and promote responsible innovation.