Do you know Supervised and Unsupervised Learning?

Supervised Learning.

The training data fed to the algorithm has the actual outputs associated with them (labels).

A simple example of this is email spam detection. The algorithm is trained with many example emails along with their class label and it must learn how to classify new emails.

This was a typical task of classification, another task for which the target values are continuous numerical is called regression.

A set of features is fed into the algorithm (X) and actual outputs are numerical values (Y).

Some of the supervised learning-based algorithms are:

Linear - Regression

Logistic Regression

K-Nearest Neighbors

Decision Trees

Random Forest

Neural Networks

Support Vector Machines

Un-Supervised Learning

Here the training data is not labeled. Suppose you want to segregate customer groups or find out similarity among groups of data which might not be expected.

Another task is of Dimensionality Reduction. If your dataset consists of many features then without losing significant information you can combine features using certain algorithms. If dimensions (features) in your data can be reduced do it, as it makes the model much simpler and faster.

Some of the unsupervised learning-based algorithms are:

K-means clustering

Hierarchical clustering

Principal Component Analysis (PCA)

Kernel PCA

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