Machine Learning Notes

 Supervised Learning

Machine learning can be branched out into the following categories:

  • Supervised Learning
  • Unsupervised Learning

Supervised Learning is where the data is labeled and the program learns to predict the output from the input data. For instance, a supervised learning algorithm for credit card fraud detection would take as input a set of recorded transactions. For each transaction, the program would predict if it is fraudulent or not.

Supervised learning problems can be further grouped into regression and classification problems.

Regression:

In regression problems, we are trying to predict a continuous-valued output. Examples are:

  • What is the housing price in Neo York?
  • What is the value of cryptocurrencies?

Classification:

In classification problems, we are trying to predict a discrete number of values. Examples are:

  • Is this a picture of a human or a picture of an AI?
  • Is this email spam?

For a quick preview, we will show you an example of supervised learning.


Unsupervised Learning

Unsupervised Learning is a type of machine learning where the program learns the inherent structure of the data based on unlabeled examples.

Clustering is a common unsupervised machine learning approach that finds patterns and structures in unlabeled data by grouping them into clusters.

Some examples:

  • Social networks clustering topics in their news feed
  • Consumer sites clustering users for recommendations
  • Search engines to group similar objects in one cluster

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