What Is Machine Learning?

Are you familiar with the heavily hyped, self-driving Google car? How about online recommendation offers such as those from Netflix and Amazon? Well, these are just some examples of machine learning applications. But how does machine learning work? Let’s find out. 

Understanding Machine Learning

Machine learning (ML) is a branch of computer science and artificial intelligence (AI) that enables computers to learn automatically from past data or experiences. It uses various algorithms to create mathematical models and make predictions using historical information or data.

How Does Machine Learning Work?

At its heart, ML algorithms analyze and look for patterns from datasets and then use this information to make better predictions on new data sets. It’s similar to how a person learns and improves. Whenever you make a decision, you may consider your past experiences to evaluate the situation better. An ML algorithm does the same by analyzing past data to make decisions or predictions. After all, ML is an AI application that allows computers or machines to self-learn from data. 

To get a simple understanding of how ML works, think about how you would learn to play the dinosaur game—a survival game in Google Chrome that appears when you try to visit a website while disconnected from the internet. If you haven’t played the game before, you need to jump by pressing the spacebar whenever the T-Rex approaches a cactus plant and duck when it encounters a bird. As a human being, you would use the trial-and-error strategy to learn how to play the game. By playing the game several times, you can easily understand that to win the game, you should avoid running into the bird or the cactus. 

Types of Machine Learning

There are four main techniques that people use in machine learning:

Supervised Machine Learning

In supervised ML, you act as a guide and teach the machine what conclusions it should make. Just as children learn to identify fruits by memorizing them in a picture book, in supervised ML, the machines are trained by datasets that are already labeled and have a predefined output. Some examples of supervised ML include algorithms such as logistic and linear regressions, support vector machines and multiclass classification. 

Unsupervised Machine Learning

Unlike supervised ML, unsupervised ML uses a more independent approach. It enables a computer to learn to identify complex patterns and processes without someone providing close, constant guidance. Unsupervised ML involves training based on data that doesn’t have labels or specific, defined output. 

In the child teaching analogy, unsupervised ML is similar to a child learning to identify fruits by observing patterns and colors instead of memorizing the names with a teacher’s help. The child may look for similarities between pictures and categorize them, assigning each category its new label. Some examples of unsupervised ML algorithms include association rules, independent and principal component analysis and k-means clustering. 

Semi-supervised Machine Learning

Semi-supervised ML comprises characteristics of both supervised and supervised ML. It uses a combination of labeled and unlabeled datasets to train machines. For example, a student learning a concept under the supervision of a teacher is referred to as supervised learning. In unsupervised learning, a student understands the same concept alone at home without the teacher’s assistance. Meanwhile, a student revising the concept after learning under the supervision of a professor is a semi-supervised form of learning. 

Reinforcement Machine Learning

Reinforcement ML allows an AI-driven system (also called an agent) to learn through trial and error using feedback from its actions. This feedback is either positive or negative, signaled as reward or punishment with the aim of maximizing the reward function. Reinforcement ML learns from its mistakes and offers AI that imitates natural intelligence as closely as it’s currently possible. 

How Different Industries Use Machine Learning

Companies across industries are using ML in various ways. Here are some examples of ML in key sectors:

Healthcare

ML is being increasingly adopted in the healthcare sector, credit to wearable devices and sensors, such as smart health watches and wearable fitness trackers. All such devices track users’ health data to assess their health in real-time. 

Retail 

Retail websites use ML extensively to recommend items based on users’ purchase history. Retail companies use ML techniques to capture information, analyze it and provide personalized shopping experiences to their customers. They also use ML for price optimization, customer merchandise planning, customer insights and marketing campaigns. 

Finance

Today, several banks and other financial institutions use ML technology to address fraudulent activities and draw essential insights from enormous volumes of data. ML-derived insights help investors make better trading decisions.