Machine Learning API Libraries That are Helping the IoT

Tularam M. Bansod is proprietor of TMB Enterprises, a startup focussed on embedded systems domain. He has authored two books on microcontroller programming.


In this article, we learn how machine learning (ML) can make things better when it comes to a complex concept like the Internet of Things (IoT).

Machine learning (ML) is becoming the new buzzword in the industry. Neural networks and cognitive science have come up with a number of new algorithms. ML techniques are heavily used in image, voice, video, public safety and medical fields, among others. It can be implemented at human scale for predictions, decision-making processes and so on.

In ML, a system can learn from data continuously in real time. Continuous learning makes the system intelligent enough to produce multiple logics if newly observed data is available. These multiple logics help run driverless (autonomous) cars intelligently on the road.

There are two basic types of ML: supervised and unsupervised. In supervised learning, past knowledge is applied to draw inferences from new data sets. Whereas, in unsupervised learning, past knowledge is not available. Technically, the system does not figure out the right output, but it explores the data and can draw inferences from data sets to describe hidden structures from unlabelled data.

Popular ML techniques are regression, recommendation, clustering and classification. All these algorithms have their pros and cons, and work based on the statement of problem and data sets. There are four steps to apply ML to a project/problem: define a problem carefully, define data, evaluate the algorithm and improve results.