• Supervised learning — the machine is presented with a set of inputs and expected outputs, later given a new input the output is predicted.
  • Unsupervised learning — the machine aims to find patterns, within a dataset without explicit input from a human as to what these patterns might look like.



  • The number of clusters (k) must be given explicitly. In some cases, the number of different groups is unknown.
  • k-means iterative nature might lead to an incorrect result due to convergence to a local minimum.
  • The clusters are assumed to be spherical.


Dimensionality Reduction

  • Selecting from the existing features (feature selection)
  • Extracting new features by combining the existing features (feature extraction)





Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Decision Tree!

Using the Right Performance Metrics

ViVoVAD: A Voice Activity Detection Tool Based on Recurrent Neural Networks

Neural Network Tutorial —Multi Layer Perceptron

Feature Selection (Boruta /Light GBM/Chi Square)-Categorical Feature Selection

Predicting offer-completeness on Starbucks offers

TensorFlow Object Detection on Windows

Car Localization and Counting with Overhead Imagery, an Interactive Exploration

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store



More from Medium

How to Map OneDrive to a Drive Letter in Windows 11?


GHOSTLIGHT STUDIOS presents Remember; a new short film

[Session] 83% WR , 6 Trades, 18 Jan