Find out how machine learning propels the technology you use most! This easy read will teach you about supervised, unsupervised, and reinforcement learning.
Ever Wondered How Spotify Comes up with that almost perfect playlist or how Siri can almost lip-read you so well? The answer is in Machine Learning, or ML for short, a revolutionary technique that enables the computer to learn on its own through the data that is fed into it.
Machine learning is a technique through which the systems learn using big data and perform better while making some decisions without being programmed for each one of them.
From suggesting what program to watch on Netflix to forecasting the stock or voting behaviour, ML lies at the heart of most activities in today’s world.
Let’s break ML algorithms into three main types: supervised learning, unsupervised learning, and reinforcement learning.
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#1: Supervised Learning: The Teacher's Pet
Supervised learning might be best described as a student accompanied by a manual or guide. It is built based on the labelled data, where inputs may be questions, and corresponding to these questions, there are specific outputs that can be answered. Let’s say we have an input, an image of an umbrella; then the trained ML can identify it and respond, “This is an umbrella.”
The algorithm learns based on the existing solutions, and the relationship between the two is made apparent from such an approach.
For instance, in the case of house prices, an algorithm is prepared to consider input variables such as location, size, and output variables like actual prices (Hastie et al., 2009). Once trained, the model can then predict prices for new properties on the same patterns that have been learned.
Applications: Spotting spam emails, diagnosing illnesses, or predicting sales trends.
#2: Unsupervised Learning: The Explorer
Consider yourself in a hall with absolutely unknown people and separating them in your mind based on the hat they are wearing or the conversation they are having. Yes, it is completely okay, and this is what we refer to as unsupervised learning, which is what we are doing here.
“Artificial intelligence is the new electricity.” — Andrew Ng
It looks for patterns in data, and all of them are unlabelled. For instance, firms use it to sort customers by their purchase frequency (Murphy, 2012) with the aim of marketing particular clients.
Application: Market segmentation, image compression, or finding outliers in data.
#3: Reinforcement Learning: Trial and Error
Reinforcement learning is similar to training a pet on tricks; it works by acting and getting positive reinforcement for the right actions or penalties for the wrong ones. It achieves results, communicates with the environment, and improves as time goes on (Sutton & Barto, 2018). Thus, reinforcement learning can make machines adaptive and intelligent and the best solution for real-time problems.
Application: Teaching robots, mastering video games, or managing energy grids.
Take Away
Supervised, unsupervised, and reinforcement learning means that machine learning drives better applications, processes, and innovation. It’s the science underlying that prediction in trends as well as the movement of your robotic wheelchair or the way your favourite app does.
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