Main Types of Machine Learning: What Are the Differences Between Them?

The main types of machine learning are the supervised, semi-supervised, and unsupervised learnings which assist in the effective operation and handling of technological products for efficiency. Each of these types has their own differences and these differences provide the diversity that comes with the operations.

Here are the three machine learning types available:

1. Supervised learning aims to learn a function that, given a sample of data and desired outputs, approximates a function that maps inputs to outputs.
2. Semi-supervised learning aims to label unlabeled data points using knowledge learned from a small number of labeled data points.
3. Unsupervised learning does not have (or need) any labeled outputs, so its goal is to infer the natural structure present within a set of data points.

Concept of Machine Learning

The concept of machine learning is a study of using data and algorithms to mimic human learning, allowing machines to improve over time, becoming increasingly accurate when making predictions or classifications or uncovering data-driven insights.

It works in three basic ways, starting with using a combination of data and algorithms to predict patterns and classify data sets, an error function that helps evaluate the accuracy, and then an optimization process to fit the data points into the model best.

Machine learning is already used around us and you may not realize how it impacts your life. Here’s a few ways it’s used that you should know:

• Social media features: Social media platforms integrate machine learning algorithms to help deliver personalized experiences to you. Facebook notes your activities, including your comments, likes, and the time you spend on different types of content. The algorithm learns from your activity and makes pages and friend suggestions tailored to you.
• Virtual assistants: Apple’s Siri, Amazon’s Alexa, and Google Now are all popular options if you’re looking for a virtual personal assistant. These voice-activated devices can do everything from search for flights to check your schedule to set alarms and more. Machine learning is a key component of these smart devices and speakers. They collect information and refine it each time you interact with them. The machine can then use that data to give you results that are best matched to your preferences.
• Recommendation engines: Popular among e-commerce websites, product recommendations are a common machine learning application. It lets these sites track your behavior based on input variables such as your searches, previous purchases, and your shopping cart history to make suggestions and recommendations about products you may be interested in.
• Image recognition: This complex technology is cropping up in a variety of fields. In your everyday life, you’ve probably come across this while uploading a photo to your social media platform. When you tag someone in an image, the platform recognizes them. It can also be transformative for identifying potential threats or criminals, unlocking phones and mobile devices, and finding missing persons.

The Types and Differences

Machine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The three machine learning types are supervised, unsupervised, and reinforcement learning.

• Supervised Learning

Gartner, a business consulting firm, predicts supervised learning will remain the most utilized machine learning among enterprise information technology leaders through 2022. This type of machine learning feeds historical input and output data in machine learning algorithms, with processing in between each input/output pair that allows the algorithm to shift the model to create outputs as closely aligned with the desired result as possible. Common algorithms used during supervised learning include neural networks, decision trees, linear regression, and support vector machines.

This machine learning type got its name because the machine is “supervised” while it’s learning, meaning you’re feeding the algorithm information to help it learn. The outcome you provide the machine is labeled data, and the rest of the information you give is used as input features.

For example, if you were trying to learn about the relationships between loan defaults and borrower information, you might provide the machine with 500 cases of customers who defaulted on their loans and another 500 who didn’t. The labeled data “supervises” the machine to figure out the information you’re looking for.

Examples

• Predicting real estate prices
• Classifying whether bank transactions are fraudulent or not
• Finding disease risk factors
• Determining whether loan applicants are low-risk or high-risk
• Predicting the failure of industrial equipment’s mechanical parts

• Semi-supervised Learning

Semi-supervised learning also known as reinforcement learning is the closest machine learning type to how humans learn. The algorithm or agent used learns by interacting with its environment and getting a positive or negative reward. Common algorithms include temporal difference, deep adversarial networks, and Q-learning.

Going back to the bank loan customer example, you might use a reinforcement learning algorithm to look at customer information. If the algorithm classifies them as high-risk and they default, the algorithm gets a positive reward. If they don’t default, the algorithm gets a negative reward. In the end, both instances help the machine learn by understanding both the problem and environment better.

Gartner notes that most ML platforms don’t have reinforcement learning capabilities because it requires higher computing power than most organizations have. Reinforcement learning is applicable in areas capable of being fully simulated that are either stationary or have large volumes of relevant data. Because this type of machine learning requires less management than supervised learning, it’s viewed as easier to work with dealing with unlabeled data sets.

Examples

Practical applications for this type of machine learning are still emerging. Some examples of uses include:

• Teaching cars to park themselves and drive autonomously
• Dynamically controlling traffic lights to reduce traffic jams
• Training robots to learn policies using raw video images as input that they can use to replicate the actions they see

• Unsupervised Learning

While supervised learning requires users to help the machine learn, unsupervised learning algorithms don’t use the same labeled training sets and data. Instead, the machine looks for less obvious patterns in the data. Unsupervised machine learning is very helpful when you need to identify patterns and use data to make decisions. Common algorithms used in unsupervised learning include Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models.

Using the example from supervised learning, let’s say you didn’t know which customers did or didn’t default on loans. Instead, you’d provide the machine with borrower information and it would look for patterns between borrowers before grouping them into several clusters.

Examples

Unsupervised algorithms are widely used to create predictive models. Common applications also include clustering, which creates a model that groups objects together based on specific properties, and association, which identifies the rules between the clusters. A few example use cases include:

• Creating customer groups based on purchase behavior
• Grouping inventory according to sales and/or manufacturing metrics
• Pinpointing associations in customer data (for example, customers who buy a specific style of handbag might be interested in a specific style of shoe)