#reinforced_learning

We are using machines to predict results based on incoming data. Basically, we have three types of ML: a) Supervised Learning b) Unsupervised Learning c) Reinforcement Learning

a) In Supervised Learning (having data with labels), the data we received already has categories. eg csv or excel files, with rows and columns having well-labeled data. In supervised learning scenario, we generally use a classification or regression model. In classification model, we can decide whether a fruit is an apple or mango based on input labels. In regression model, based on inputs, we can predict the outcome. eg., we can select candidates in interview based on inputs labels like qualification, skills, experience etc provided in a resume.

b) In Unsupervised Learning, sometimes we get data that doesn’t have labels or isn’t classified, allowing the model to act without any guidance, which is called unsupervised learning. Here the task of the model is to group together unsorted data according to similarities, patterns, difference etc without any prior training. We can discover inherent groupings in the data, such as grouping customers on the basis of historic purchasing behavior. We can use a clustered Learning model and and Association Rule Learning model. In clustering model, we are grouping customer by their purchasing behavior whereas Association Rule Learning model, we make an association such that we make an inference such that a customer who purchases Product A is most likely to purchase Product B as well and group them together.

c)Reinforced Learning is applicable for skill acquisition or Real time Learning eg learning a game by playing it hundreds of times till it achieves the skill level. it learns the art through trial and error.

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