What in the AI/ML?!

A simple roadmap of all learning methods in Machine Learning (ML) Think of a robot trying to become smarter. There are only a few basic ways we can teach it: 1. Supervised Learning You show the machine examples and give it the answers. It learns by comparing its guesses to the correct answers and adjusting. Example: You show it 100 photos of cats and dogs labeled as “cat” or “dog”. It learns to tell them apart. We'll dive into this next! 2. Unsupervised Learning You give it data, but no answers. It tries to find patterns or group similar things. Example: You give it 1,000 customer reviews with no ratings. It learns to group similar ones together (like “angry” vs. “happy”). 3. Reinforcement Learning (RL) You give it a goal, and it learns by trial and error. It gets rewarded or punished and learns what works. Example: A robot in a maze learns the right path by getting points for moving closer to the exit. 4. Self-Supervised Learning It creates...

Imagine this first: Let’s say you want a machine to help you clean your house. AI (Artificial Intelligence) is the idea that the machine can “act smart”—like decide what to clean first. ML (Machine Learning) is the part where the machine learns how to clean better by practicing over time. That’s the difference in real life: AI = the brainy behavior ML = the learning process inside Let’s break it down even more: Concept What it means Easy example AI (Artificial Intelligence) Any computer or robot that does “smart” things like humans Voice assistants, self-driving cars, chatbots ML (Machine Learning) A way for machines to “learn” by seeing data again and again Netflix learning what shows you like Think of AI like the goal (be smart), and ML like the tool (how it learns to be smart). Visual Analogy: AI = A finished puzzle ML = The method of putting pieces together ML is how AI ...