Which Machine Learning Technique Should You Use?

Change is inevitable in technology and every passing day is an evidence to support this fact. You are out of touch for a while and when you return, you hear about some new Software or feature update that has taken place. This is a reality that we all are getting used to. An example of how swiftly technology advances is machine learning.

A decade ago, who would have thought of machine learning making such an impact and being embraced at such a pace. The success that it enjoys is a result of the benefits it offers. It has established itself as one of the most reliable ways of using big data and reaping fruits. There are plenty of resources that can explain how machine learning works, so in this blog, I will not discuss the neural networks, logic networks, etc. I will focus on the types of machine learning with a reference to the popular algorithms.

What Are the Popular Types?

The two commonly heard terms when talking about machine learning are supervised and unsupervised. Now, these are exactly what the words mean. In supervised machine learning, you use algorithms to decide the type of results you require. The data in this case might need to be clustered or arranged in a specific order and the tool will help you identify the pattern based on the features that you have set. Some of algorithms used in supervised machine learning are:

  • Support Vector
  • Naïve Bayes Classifier

An example to explain supervised machine learning can be to think of a global brand with multiple outlets across the world which wants to use machine learning to identify shopping patterns for the last 20 years. The brand is looking for specific information that would draw a comparison between the male and female shirts sold in that tenure. In this case, supervised machine learning would work just fine because the result required is conditional.

The other alternative is unsupervised machine learning. In this case, it is not important for you to set filters or attributes. The tool can be used without pre-set features. The tool will organize the data by identifying patterns that you do not have to describe. This is a great way of managing large data sets that are not clustered or labelled. Some popular unsupervised machine learning algorithms are:

  • K-Means
  • Apriori

To illustrate this, let’s think of how a search engine functions. The data pool is big and when a user searches for something like aviator, the results might include the popular Hollywood movie, sunglass style, or even a two-wheeler manufactured with the name. An unsupervised machine learning can classify the results into the labels mentioned above and users can view the results only for the label that they are interested in.

Which One Should You Use?

The answer to this question will be on based on your answer to what are you expecting your machine learning tool to do. If you are looking for simple clustering or filtering of data on the basis of pre-set features, then supervised machine learning will be of use. But, if you want to use deep learning or machine learning literally, then unsupervised is the one meant for you.

As discussed above, unsupervised machine learning tools have the ability to recognize patterns without any pre-feeding or filtering. These tools can provide insights on matters with maximum relevance and can learn from the mistakes that it makes.

The right type of machine learning depends on the type of data that you are dealing with and the type of results you expect. There are other methods like semi-supervised machine learning that uses the best of both and as mentioned maybe by the time you are reading this blog some new type has emerged. If you are still unsure of which way to go, connect with a software application development expert who can guide you to the right path.

You might like

About the Author: Vijay Aegis

We use cookies in order to give you the best possible experience on our website. By continuing to use this site, you agree to our use of cookies.