Supervised and Unsupervised Learning Algorithms

In the previous chapter, we got some insight into the various aspects of machine learning and were introduced to the various ways in which machine learning algorithms could be categorized. In this chapter, we will go a step further into machine learning algorithms and try to understand supervised and unsupervised learning algorithms. This categorization is based on the learning mechanism of the algorithm, and is the most popular.

In this chapter, we will be covering the following topics:

• An introduction to the supervised learning algorithm in the form of a detailed practical example to help understand it and its guiding principles
• The key supervised learning algorithms and their application areas:
  • Naive Bayes
  • Decision trees
  • Linear regression
  • Logistic regression
  • Support vector machines
  • Random forest
• An introduction to the unsupervised learning algorithm in the form of a detailed practical example to help understand it
• The key unsupervised learning algorithms and their application areas:
  • Clustering algorithms
  • Association rule mapping
• A broad overview of the different mobile SDKs and tools available to implement these algorithms in mobile devices