Explore the core types of machine learning – Supervised, Unsupervised, and Reinforcement Learning. Understand their differences, how they work, and their real-world applications.
Table of Contents
Question
What are the three types of Machine Learning?
A. Supervised learning, unsupervised learning, and overclocked learning.
B. Super-parsed learning, previewed learning, and retroactive learning.
C. Supervised learning, unsupervised learning, and reinforcement learning.
Answer
C. Supervised learning, unsupervised learning, and reinforcement learning.
Explanation
Machine Learning is categorized into supervised learning (which uses labeled data), unsupervised learning (relying on unlabeled data), and reinforcement learning (which uses trial and error).
Supervised learning is a type of machine learning where the model is trained on a labeled dataset. The model makes predictions based on this training and its performance is then evaluated on a test set.
Unsupervised learning, on the other hand, involves training a model on an unlabeled dataset. The model identifies patterns and structures in the data without any prior knowledge of the correct output.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions, rather than from being explicitly taught.
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