This module introduces the fundamentals of deep learning, a powerful branch of machine learning that uses neural networks inspired by the human brain. It explains how these systems learn from data layer by layer and power technologies like voice assistants, image recognition, and language models. By exploring both the strengths and limitations of deep learning, students gain a clear understanding of how this technology works—and why it matters.
This module introduces the core ideas behind machine learning, explaining how machines learn from data without explicit programming. It covers key ML problem types, the training process, and real-world challenges like bias and overfitting. A great starting point for understanding how intelligent systems recognize patterns and improve over time.
Artificial intelligence learns not through memorization but by identifying patterns in data. This module explores the three main types of machine learning—supervised, unsupervised, and reinforcement—and explains how each allows AI systems to improve over time. Understanding these methods helps us grasp both AI’s power and its ethical challenges.
Artificial Intelligence exists on a spectrum — from simple reactive machines to the idea of fully self-aware systems. This module introduces the four main types of AI: reactive, limited memory, theory of mind, and self-aware. Understanding these types helps us grasp where AI stands today and what its future might hold.
Artificial Intelligence (AI) is the science of building machines and systems that can think, learn, and make decisions like humans — or sometimes even better. From virtual assistants to self-driving cars, AI is transforming how we live, work, and connect. But it’s not just about speed or automation; it’s about teaching machines to understand, reason, and adapt. This module will guide you through what AI really is, how it works, and why it matters.