Categories and Tasks of Machine Learning

Learn different machine learning paradigms.

In this lesson, we'll look at how the ML process can be classified based on its category and the task that it needs to perform.

Categories of machine learning

There are several ways ML can be categorized. First, there are three types of ML, which can be summarized as follows:

  • Supervised learning: This is a type of ML where the algorithm is trained on labeled data. Labeled data is data that has been preclassified with the correct output or target value. The goal of supervised learning is to use the labeled data to learn a function that can predict the correct output for new input data.

  • Unsupervised learning: In unsupervised learning, the algorithm is trained on unlabeled data. The goal of unsupervised learning is to discover patterns in the data without any preexisting knowledge of the output. Clustering, anomaly detection, and dimensionality reduction are common tasks in unsupervised learning.

  • Reinforcement learning: This is a type of ML where the algorithm learns by interacting with the environment. The algorithm learns by receiving feedback in the form of rewards or punishments for certain actions taken in the environment. The goal of reinforcement learning is to find an optimal policy or set of actions that maximize the cumulative reward over time.

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