Supervised, Unsupervised and Reinforcement Learning
Ever wondered how amazon suggests you various products on its website or ever noticed how Facebook and Instagram recommend your new friends which are mutual. This all happens with Machine Learning Algorithms. Let’s take a quick look at the various types of Machine Learning.

Supervised Learning

Supervised means to oversee or direct a certain activity and make sure it’s done correctly. In this type of machine learning, labeled data is used to teach the match and the machine is told explicitly that this is the input and this is exactly how the output must look. There are various algorithms that are used in supervised learning:

Linear Regression

Logistic Regression

Support Vector Machine

K nearest Neighbour

Random Forest

Why is it important?

Supervised Learning turns data into helpful insights. It helps organizations to understand and prevent certain situations which may hamper the growth of that organization while at the same time helps them to boost desired outcomes.

Unsupervised Learning

Unsupervised machine learning is a type of Machine Learning algorithm which is used to draw conclusions from a dataset consisting of input data without labeled outputs. The most common type is the clustering technique which helps to find hidden patterns or groups in the dataset. Unsupervised Learning can perform more complex problems than supervised learning. There are two categories of algorithms in unsupervised learning:

Clustering : A clustering problem is where we identify the groups or clusters based on certain properties.

: A clustering problem is where we identify the groups or clusters based on certain properties. Association: An association problem is where we identify the rules which are followed by a large portion of data

Why is it important?

It helps to determine patterns that were previously unknown.

Patterns help in categorization or finding associations.

They can identify anomalies and defects in the data.

They work on unlabeled data which makes it easier to work.

Applications

Recognize patterns to cluster data

Defects in the data collected

Identify Dependencies

Cleansing the datasets by removing unwanted features

Reinforcement Learning

Reinforcement means to establish or encourage a pattern of behavior. This type of machine learning trains the models to make a sequence of decisions to reach the ultimate goal. It uses error and trial methods to come up with the final solution to the problem. The machine is then either rewarded or penalized based on its actions performed. It entirely depends on the model/machine on how to perform the task to maximize its reward.

Why is it important?

RL models learn through a continuous process wherein they receive rewards/ penalties on every action it performs so this helps the machine/ model to tackle unforeseen situations and train accordingly.
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['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Artificial Intelligence']