Introduction
Before using modern AI tools, it's essential to understand the underlying principles of Machine Learning (ML). Unlike traditional programming where you write explicitly defined rules, ML involves training an algorithm on data so it can discover patterns on its own.
Types of Machine Learning
Machine Learning is generally categorized into three main types:
- Supervised Learning: The model is trained on labeled data. For example, feeding thousands of images of cats and dogs, each tagged correctly, so the model learns to identify them.
- Unsupervised Learning: The model looks for patterns in unlabeled data. Clustering customers based on purchasing behavior is a common example.
- Reinforcement Learning: The model learns by trial and error in an environment to achieve a goal, maximizing a reward. This is how many game-playing AIs are trained.
Deep Learning and Neural Networks
Deep Learning is a specialized subset of Machine Learning based on Artificial Neural Networks. Inspired by the human brain, neural networks consist of layers of interconnected nodes (neurons). Deep learning powers modern AI breakthroughs, including computer vision and natural language processing.
Assignment
- Read this introductory article by IBM on Supervised vs. Unsupervised Learning.
- Watch 3Blue1Brown's video explaining Neural Networks to beginners.