What Is Machine Learning?

Machine Learning

Computer machines learning to learn on their own is what seems to be the eye-catcher for researchers worldwide.  This is probably why you’ve been hearing the term Machine Learning a lot these days.

But, let’s not get confused with Machine Learning and AI. As compellingly similar as one might find them, they are actually two very different concepts that go hand-in-hand. Before we dive into the sea of Machine Learning, it’s better to get clarified first.

Artificial Intelligence is an umbrella term that deals with developing machines that can carry out tasks in a smart and intelligent way. This usually includes programming or giving step-by-step instructions to the system. Machine Learning is where your computer learns on its own without the need for explicit programming.

Now, I bet you can already imagine the plethora of possibilities when these two are combined.

What Is Machine Learning?

Machine Learning is the process of teaching the machines or the computer systems to learn on their own either from past experiences or from scratch. Clearly, Machine Learning is an application of Artificial Intelligence that only makes it much more convenient to produce human-like machines.

There are three major types of learning that you need to be well aware of when dealing with Machine Learning. They include Supervised Learning, Unsupervised Learning and Reinforcement Learning.

SUPERVISED LEARNING

In this type of learning, the machine is exposed to a set of training data which comprises of training examples. These examples include ideal input-output pairs for the system to get familiar with the kind of data it will be dealing with in the future and with the possibilities of output that are likely to be accepted.

The perfect yet, simple example to help you understand this type of learning is your email’s very own spam filter! The spam filter is designed in such a way that it tells the differences between emails that are useful to you and that are nothing but junk wasting your space.

Based on how you interact with your emails, whether you read or delete them, your spam filter can make better predictions in the future.

UNSUPERVISED LEARNING

The unsupervised method enforces learning with the help of unlabelled and uncategorized data.  Unlike supervised learning, there is no training data or learning from previous experiences involved here. The main aim of unsupervised learning is to find out the underlying hidden structure in the data so that it can more from it. It’s up to the machines to discover and learn interesting data. Let us look at a real-life example to obtain a better understanding. Say, you take a toddler to the zoo and show him the various animals in the cages but, you don’t name them.  Without knowing their names, the toddler only remembers all the animals he saw based on their physical characteristics such size, colour etc. The next time he sees a similar animal he won’t be able to name but he’ll be able to match it with one of the previous animals he saw. This is basically how unsupervised learning works.

REINFORCEMENT LEARNING

Reinforcement Learning, in other words, can be called as the trial and error method. This type of learning involves an agent that interacts with the environment and based on the actions performed a reward is given. Rewarded actions signify the desired/correct outputs so that the agent knows what to do better the next time. To break it down even further, let’s take a simple situation. You see a fireplace and you move closer towards it and you feel warm. This is a reward and now you know this action is acceptable. Now, if you move even closer and touch the fire, it burns you. This is not a preferred action. Through the process of trial and error, you’ve come to a conclusion that the fire can only be enjoyed safely from a distance. Making the machines follow this human approach is what reinforcement learning is all about.

PLETHORA OF BENEFITS

Machine Learning is such a powerful tool that makes use of self-teaching algorithms which in a way makes machines more similar to humans. Several world class organizations like Amazon and Flipkart make use of machine learning in better catering of services to their customers. Based on the kind of products you search for, these online shopping sites are able to recommend similar products that you might be interested in the next time you visit. Facebook also uses a similar technique to show relevant ads to its users. Your everyday Personal Assistant also implements machine learning concepts. Your interactions with the AI assistant helps her to understand and respond to you in better ways and become your personalized assistant. Apart from this, Machine Learning is also used in a variety of other fields including healthcare, banking and finance, agriculture, education and much more.

CAREER OPPORTUNITIES

Machine Learning is one of the cutting-edge technologies that is trending right now. With a career in Machine Learning, you’ll always be on the verge of finding the ‘Next Big Thing.”  

A curious mind with a passion for innovation combined with technical knowledge will land you a high paid job as a Machine Learning engineer. Machine Learning is so widely preferred that I has become a vital part of several organizations. In the recent launch of Pixel 2, Google has incorporated machine learning techniques to create a major difference when to compared to the first generation Pixel phones. Pixel 2 has an all-new feature that uses machine learning to recognize the music that is being played and the name of the song and the artist will be displayed on your lock screen to make things easier for you. This Now Playing feature can recognize up to 70,000 songs which are weekly updated on the Google Play Music by using a  miniaturized neural network. If you still find yourself hesitant to step into this career field, here is what the Father of Microsoft, Bill Gates had to say about ML, “A breakthrough in machine learning would be worth ten Microsofts.” You will be awestruck with the what PayScale confirms you’ll earn as a Data Scientist with machine learning skills.

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