Machine Learning Paradigms
Explore the three main paradigms of machine learning including supervised learning where models train on labeled data, unsupervised learning which discovers patterns in unlabeled data, and reinforcement learning where agents learn optimal behaviors through rewards. Understand how these paradigms apply to real-world examples like classification, clustering, and robotics.
Machine learning can be divided into three categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised machine learning
In supervised learning, the training data features are provided as input to the algorithm which also includes the output labels. The algorithms map the input to the output by using the provided input-output pairs.
Some common examples of supervised Machine learning are:
Classification
A machine is trained to classify the given data into a class.
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