3 Types of Machine Learning You Should Know

Machine learning is a specialised technology that falls under the umbrella of artificial intelligence (AI). This exciting field is the driving power behind many modern technologies, including image recognition, self-driving cars, and products like Amazon's Alexa.

The global machine learning (ML) market is predicted to grow to USD 225.91 billion by 2030, up from USD 26.03 billion in 2023, according to Fortune Business Insights [1]. This rapid growth means there is plenty of opportunity to dive into a career in machine learning.

When you decide to start the journey into machine learning, you should know three main types of machine learning. Read on to learn more.

What is machine learning?

This branch of AI focuses on 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 combines data and algorithms to predict patterns and classify data sets in three basic ways. This error function helps evaluate the accuracy and then an optimisation process to fit the data points into the model best.

Did you know?

Arthur Samuel created the term "machine learning" in his research in the early 1960s. That research was based on the checkers game that Robert Nealy played against an IBM 7094 computer and lost. Although this is minor compared to what machines can do today, it was a groundbreaking milestone at the time.

Applications of machine learning 

Machine learning is already used around us, and you may need to realise how it impacts your life. Here are a few ways it's used that you should know:

Social media features: Social media platforms integrate machine learning algorithms to help deliver personalised experiences to you. Facebook notes your activities, including your comments, likes, and time spent 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 search for flights, check your schedule, 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 results that best match your preferences.

Product recommendations: Popular among e-commerce websites, product recommendations are a common machine learning application. It lets these sites track your behaviour based on your searches, previous purchases, and shopping cart history to make suggestions and recommendations about products you may be interested in.

Image recognition: This complex technology is cropping up in various fields. You've probably seen this daily while uploading a photo to your social media platform. When you tag someone in an image, the platform recognises them. It can also be transformative for identifying potential threats or criminals, unlocking phones and mobile devices, and finding missing persons.

3 types of machine learning  

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

1. Supervised learning

Gartner, a business consulting firm, predicts that supervised learning will remain the most utilised machine learning among enterprise information technology leaders in 2022 [2]. This type of machine learning feeds historical input and output data in machine learning algorithms, with processing between each input/output pair that allows the algorithm to shift the model to create outputs as closely aligned with the desired result. 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 learning, which means you’re feeding the algorithm information to help it learn. The outcome you provide the machine is labelled data, and the rest of the information you give is used as input features. 

For example, suppose you were trying to learn about the relationships between loan defaults and borrower information. In that case, you might provide the machine with 500 cases of customers who defaulted on their loans and another 500 who didn't. The labelled data "supervises" the machine to determine your desired information.

Supervised learning is effective for various business purposes, including sales forecasting, inventory optimisation, and fraud detection. Some examples of use cases include:

• 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

2. Unsupervised learning

While supervised learning requires users to help the machine, unsupervised learning doesn't use the same labelled training sets and data. Instead, the machine looks for less obvious patterns in the data. This machine learning type is very helpful when you must 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.

This type of machine learning is widely used to create predictive models. Common applications also include clustering, which creates a model that groups objects 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 behaviour

• 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)

3. Reinforcement learning

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

Returning to the bank loan customer example, you might use a reinforcement learning algorithm to examine 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. Ultimately, both instances help the machine learn by better understanding the problem and environment.

Gartner notes that most ML platforms don't have reinforcement learning capabilities because they require higher computing power than most organisations have [2]. 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 when dealing with unlabelled data sets. 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

Career paths in machine learning 

The World Economic Forum's Future of Jobs Report 2020 predicts that machine learning and all artificial intelligence will generate 97 million new jobs worldwide by 2025 [3]. Machine learning is an in-demand field that lends itself to several possible career paths, including:

*All salary data sourced from Glassdoor as of January 2024

Machine learning engineer: You can work on machine learning projects and create and manage platforms in this role. 

• Average annual salary (IN): ₹12,00,000 [4]

Data scientist: In this role, you can use a combination of machine learning and predictive analytics to collect, analyse, and interpret data. 

• Average annual salary (IN): ₹14,00,000 [5]

Business intelligence developer: In this role, you’ll analyse data to gather insight into business and market trends. 

• Average annual salary (IN): ₹10,00,000 [6]

How to get started in machine learning

Most employers look for a combination of education and experience. Here are three common ways to set yourself on the path to the job you want: 

1. Earn a bachelor's degree.

Start your career path with a bachelor's degree in data science, computer programming, computer science, or a related field. Machine learning is an advanced field, and employers tend to hire candidates with at least a bachelor's degree. You may need a master's or PhD to qualify for some positions.

2. Gain work experience.

Try to land an internship or entry-level position in machine learning-related roles in software development, software engineering, data engineering, or data science.

3. Advance your career.

Consider earning a master's degree or brushing up on your skills with a professional certificate. Many employers prefer to hire machine learning professionals with advanced degrees in software engineering, computer science, machine learning, or AI.

Next steps

Learn the fundamentals of machine learning with Stanford’s popular Machine Learning course, or develop the skills needed to build and train deep neural networks with the Deep Learning Specialisation from DeepLearning.AI. If you’re serious about a career in machine learning, explore how a data science degree could unlock new opportunities.