Basics of Machine Learning

Types of machine learning in simple terms

  1. Supervised Learning is like learning with a teacher. You are given a set of examples (data) along with the correct answers (labels), and your goal is to learn a rule that allows you to predict the correct answer for new examples. A simple example would be learning to recognize cats in images. You are given a bunch of images (the examples) and for each one, you're told whether it's a cat or not (the labels). Then, given a new image, you need to predict whether it's a cat.

    Real-world example: Email spam filters use supervised learning. They are trained on a dataset of emails that are labeled as "spam" or "not spam", and they learn to classify new emails based on this training.

    Learning resource: Supervised Learning with scikit-learn

  2. Unsupervised Learning is like learning without a teacher. You are given a set of examples, but no answers. Your goal is to find some structure or patterns in the data. For instance, given a set of news articles, you might want to group them into clusters, where each cluster contains articles on the same topic. But nobody told you what the topics should be, or which articles belong to which topics - you have to figure this out yourself.

    Real-world example: Recommendation systems, like those used by Netflix or Amazon, often use unsupervised learning to group together similar items or users.

    Learning resource: Unsupervised Learning in Python

  3. Semi-Supervised Learning is a middle ground between supervised and unsupervised learning. You're given a large set of examples, but only some of them have labels. This is common in real-world scenarios where gathering a lot of data is easy, but labeling it is hard. For example, a social media company might have millions of posts (the examples), but only a few thousand have been labeled as "positive" or "negative".

    Real-world example: Google Photos uses semi-supervised learning to recognize people in photos. It can automatically group photos of the same person together, but it also allows users to manually correct its mistakes, which provides it with labeled data for learning.

    Learning resource: Semi-Supervised Learning with Python

  4. Reinforcement Learning is like learning by trial and error. You are not given any examples or labels. Instead, you learn by taking actions in an environment and receiving rewards or punishments. For example, a reinforcement learning algorithm could learn to play a video game by playing it over and over, and trying to maximize its score.

    Real-world example: AlphaGo, the computer program developed by DeepMind that defeated the world champion Go player, uses reinforcement learning to improve its gameplay.

    Learning resource: Intro to Reinforcement Learning

Each of these types of machine learning has its own strengths and weaknesses, and is suitable for different types of tasks. The best choice depends on the specifics of your problem and the data you have available.