The Types of Machine Learning are
- Supervised Learning
- Unsupervised Learning
- Semi-supervised learning
- Reinforcement Learning
What is Supervised Learning?
Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.
This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.
What is Unsupervised Learning?
With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.
What is Semi-supervised Learning?
This is similar to the above two, with the only difference being that it uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets — the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.
Here is a short video explaining the various types of Machine Learning:
What is Reinforcement Learning?
Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.
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