A Simple Take on Machine Learning

Machine learning is the key facilitator of almost all data driven activities performed by enterprises across industries. It is ranked among the most difficult technical skills to learn and deploy. But the foundational concept behind machine learning is a simple one. Let us see if we can build a simple understanding of machine learning and its different aspects through the course of this article.

Machines that learn

The simple and insightful nomenclature of machine learning gives us a peep into its core functionality. Machine learning engineers train machines to learn from data. Let us see how that works. Let us say, you want to train a machine to distinguish 1 rupee coins from 5 rupee coins based on their weight. Suppose, the one rupee coin weighs 4 grams and the five rupee coin weighs 10 grams. You enter the weights as features and the names of the coins as labels. So, now the machine knows that if a coin weighs 4 grams, it is a one rupee coin and if it weighs 10 grams, it is a five rupee coin. If you expose it to an unlabelled one rupee coin, it will be able label it as a one rupee coin based on its weight.

This was an example of supervised learning and this mode of machine learning is heavily used in trend prediction, stock trading, and sales.

Unsupervised learning and reinforcement learning

Let us say, you have a database full of images of different animals. There could be thousand images of cats, and ten thousand images of dogs, and a million images of different bovine animals. Labeling them would mean manually going through all of them. Instead you can use an unsupervised learning algorithm to cluster them in smaller groups with similar features.

All these animals share certain features, like all of them have four legs and a tail. However, only some of them horns, some of them have stripes on their body, and some have hooves instead of paws. The algorithm would identify these as unique features and cluster images with similar features. Thus it will actively segment the images.

Now, let us say, you provide the program with some labeled images and based on that you ask it to label all the animal segments. Every time it makes a wrong prediction you provide negative feedback and with every right prediction you reinforce the machine with positive feedback. This way the accuracy of the machine increases. This is reinforcement learning.

If this helps you wrap your head around the basics of machine learning you can delve deeper into different models like regression, classification, and dimensionality reduction and explore different kinds of algorithms. If you feel at home with statistics and have a fair share of interest in computer science, a machine learning certification might be right up your alley.

Get enrolled to a machine learning certification course and explore the ever expanding opportunities of this marvelous technology across industries around the globe.   

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