Machine Learning Algorithms

Imagine three cooking students learning to make dishes and that’s exactly how we teach computers new skills. The first student follows a recipe card covered in pictures and step by step instructions. In AI, that’s supervised learning, where each input (like a cat photo) comes with the correct answer (the label “cat”), so the model tweaks itself until its predictions match the labels every time. The second student skips the recipe, wandering the pantry to sniff out which spices and ingredients naturally pair well. That mirrors unsupervised learning, where the computer looks at unlabeled data and groups similar items on its own kind of like sorting fruits by color or taste without anyone telling it how. The third student treats each cake as a science experiment they bake one batch, see how it rises, and then adjust temperature or ingredients based on whether it flops or soars. This is reinforcement learning, where the model takes actions, gets rewards (like scores in a game), and figures out the best moves through trial and error.