Maximizing Machine Learning Potential: Understanding the Types

Yo! Let's talk about the different types of machine learning we've got out there. We're gonna break it down based on the level of supervision the algorithms receive during training. This is called Supervised/Unsupervised Learning. Here are the four main categories:

  1. Supervised Learning: This is when the training data comes with labels, which are basically the correct answers. Some important algorithms that fall under supervised learning include Linear Regression, Logistic Regression, Decision Trees, Random Forests, k-Nearest Neighbors, and some types of Neural Networks.

  2. Unsupervised Learning: When the training data is unlabeled, that's unsupervised learning for you. Some important algorithms in this category include Principal Component Analysis(PCA), k-Means Clustering, Hierarchical Cluster Analysis, and Apriori.

  3. Semisupervised Learning: These algorithms work with partially labeled data, meaning some of it has labels while some doesn't. An example of a semisupervised learning algorithm is Restricted Boltzmann machines(RBMs), which are trained in an unsupervised manner first and then fine-tuned using supervised learning.

  4. Reinforcement Learning: This one's a little different. Here, an agent learns on its own what the best strategy or policy is to get the most reward over time.

We'll go into more detail on each of these categories in future blog posts, so stay tuned! By the way, I've already used some of these algorithms to train different models, and you can check them out here.