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4 Categories of Machine Learning Algorithms
Machine learning algorithms can be divided into the following categories
- Supervised learning: In this type of learning, machines learn by example. First, the developer provides the algorithm with a sample dataset with known inputs and expected outputs. The algorithm then strives to find a method of arriving at the specified inputs and outputs.
- Semi-supervised learning: It is almost similar to supervised learning. However, semi-supervised learning uses both labeled and unlabeled data.
- Unsupervised learning: It is the opposite of supervised learning. The algorithm studies data to find trends and patterns. It is not provided with instructions or desired output. It is the machine that interprets and addresses problems alone.
- Reinforcement learning: The machine learning algorithm tries to find different answers or options by analyzing the parameters and values provided. In other words, this type of learning is based on experience.
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