Supervised vs Unsupervised learning

In machine learning, supervised learning is used when you already know what the output is, for a given input. So, you already know that the output is Y when the input is X.  Given this, the goal of supervised learning is to learn a function that gives you the relationship between X and Y.

Unsupervised learning is used when you do NOT know what the output is, for a given input. So, you do not know what Y is, for a given X input. The goal here is to infer the best relationships and pattern structures in the data.

Supervised learning mainly falls into the following categories:

1. Classification, it categorizes inputs into different classes. Examples include:
   1. Categorizing loan applicants into high, medium, and low-risk borrowers.
   2. Categorizing emails as spam or not.
2. Regression, it outputs numerical data like size, quantity, age etc. Examples include:
   1. Predicting the age of a person.
   2. Predicting the price of a house.

Algorithms: Linear regression, Logistic regression, Neural networks etc.

Unsupervised learning mainly falls into the following categories:

1. Clustering, it groups inputs based on similarity. Examples include
   1. Customer segmentation based on location, age, etc.
   2. Identifying high crime neighborhoods.
2. Dimensionality reduction, it removes redundant, unnecessary data from a dataset and keeps parts of data that really matters. It is similar to data compression. Examples include:
   1. Reducing dimensionality (columns) in computer vision training.
   2. Reduce datasets containing customer social media engagement with brands from multiple devices.

Algorithms: Hierarchical clustering, k-Means clustering, PCA, SVD etc.