Supervised Learning

We can understand this from an example. Suppose we are feeding raw inputs as an image of tomato to the algorithm. We have a supervisor who keeps on correcting the machine or who keeps on training the machine that yes it is a tomato or no it is not a tomato, things like that. So this process keeps on repeating until we get a final trained model, once the model is ready it can easily predict the correct output of a never-seen input.

Steps involved in supervised ML modeling

Unsupervised Learning

As we have already discussed that in unsupervised learning our dataset is not labeled, So if we are feeding apple, avocado, and orange as raw input data then our model will distinguish all three but it cannot tell whether a given cluster is of apple or not as it is unlabelled but any new data will automatically fit into the clusters that are formed.

Reinforcement learning

In this case, the dog is an agent that is exposed to the environment. An example of a state could be our dog sitting, and we use a specific word for the dog to walk. Our agent reacts by performing an action transition from one “state” to another “state.” For example, Our dog goes from sitting to walking. After the transition, it may get a reward or penalty in return.




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