How to Apply Machine Learning Algorithms to IoT Data?
The entire world is going bonkers over data, IoT and Artificial Intelligence. Tons of articles have spoken about the amount of data we generate every single day and numerous statistics have shown how much data we would generate by the year 2025. On this post, however, we are going to deviate a little from data generation and discuss how algorithms or concepts from other technologies would be applied to IoT data for optimizations. On one of our previous posts, we discussed Data Science algorithms with IoT data and today, it will be Machine Learning.
Machine Learning became a household term when Facebook shut down it’s Artificial Intelligence wing when one of its bots discovered a whole new language. With Elon Musk commenting on it and netizens indicating an I-Robot in the future, most of us understood what Machine Learning is all about.
On a very basic sense, machine learning in technology today is the process of elimination of human intervention wherever possible. It is allowing the data to learn patterns by itself and take autonomous decisions without a coder having to write a new set of codes. If you use your Siri, for instance, you would notice that its responses are more polished and appropriate as you keep using it. That is one of the basic applications of Machine Learning.
But when it comes to a complex concept like IoT, how would Machine Learning make things better for the Internet of Things? Every time the IoT sensors gather data, there has to be someone at the backend to classify the data, process them and ensure information is sent out back to the device for decision making. If the data set is massive, how could an analyst handle the influx? Driverless cars, for instance, have to make rapid decisions when on autopilot and relying on humans is completely out of the picture. That’s where Machine Learning comes to play with its
To determine which algorithm has to be used for a particular set of task, we need to first define the task. Some of the tasks include finding unusual data points, structure discovery, predicting categories and values, feature extraction and more.
Classifying the data sets into different tasks would make it easier for a beginner to understand the right algorithm application. For instance, to work on data structure discovery, clustering algorithms such as K-means could be used. K-means is designed to handle massive chunks of data including diverse data types. Quoting another example, the application of One-Class Support Vector Machines and PCA-based Anomaly detection algorithms are best for training data from unusual data points or data with high noises.
Without going too technical about the application of Machine Learning algorithms, if you intend to stay on the surface and take your time to understand and take in the concepts, we recommend watching this video by Hank Roark – a data scientist at H20.ai.