Supervised Learning and Unsupervised Learning

Recap

In the previous blog I talked about different types of data which will be essential to learn Machine Learning. I talked about how data is stored and the different types of data that can be collected to be used in Machine Learning.

Intro

Today I'll be talking about the main two types of Machine Learning categories i.e. Supervised Learning and Unsupervised Learning.

Supervised Learning

In Supervised Learning the dataset is a collection of labeled data. A labeled data is a dataset that contains the prediction value that we want to predict (called as target) and the values that we want to predict from (called as features).
Using the features and target from the dataset we train a Supervised Model which can predict the target value given its features.
Some of the models/algorithms includes Linear Regression, Logistic Regression, Decision Trees etc.

Unsupervised Learning

In Unsupervised Learning the dataset doesn't have labeled data and we basically use that dataset to provide a label to an unlabeled data.
The model is given the input data (features) without any predefined label or a target value, and the model tries to figure out the pattern/structure in the data that can separate the data.
The goal of Unsupervised learning is to discover the relationship in the data without any target value.
Some of the models/algorithms includes Clustering, Dimensionality Reduction, Anomaly Detection, etc.

There are 2 more types of Machine Learning Categories : Deep Learning and Reinforcement Learning. Ill talk about these in the future blogs.

Thanks for reading the blog see you guys in the next one.
If there is something you would like me to change or if I wrote something wrong be sure to notify me as I am still learning about Machine Learning.