Machine Learning And It's Types

Have you ever noticed while on any social media that you are shown ads of certain products. Or recommendations of similar products that you were looking for ! Well that's Machine Learning.

WHAT ?

Well, Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed.Thus we can say it's a subset of AI which makes predictions based on it's given data and experience

WORKING :

Machine Learning works on algorithms, using a trained dataset to create a model. introduction of new data leads to the prediction based on the model. The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set. (that's what we call Data Science).

Types :-

Machine learning is sub-categorized to three types :

Supervised Learning : Supervised Learning is the one, where you can consider the learning is guided by a supervisor(the dataset).Once the model is trained then it can make decisions on it's own. There are two main areas where supervised learning is useful: classification problems and regression problems.

Unsupervised Learning : This method allows the model to learn automatically by finding patterns and relationships in the dataset by creating clusters in it. Suppose there are 2 groups cats and dogs, now though the model can't identify either of the dogs or the cats, but while entering data it will divide the dataset into 2 groups and will add it to one of the created clusters based on the patterns and relationships. Here's an example of it.

Reinforcement Learning : It is about taking suitable action to maximize reward in a particular situation.It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.

Thank you.