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.


Author: Seenu Kamisetty

I have experience in Microservices, Deep Learning, Cloud, Docker, Mobile, Java, Scala, Python. I greatly enjoy helping businesses to take advantage of technologies. Check me out on Twitter @AddictedAi

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