3 Types of Machine Learning You Should Know

Machine learning (ML) is a subset of artificial intelligence (AI) focused on creating intelligence applications, machines, and systems capable of making decisions without human intervention. This exciting field is the driving power behind many modern technologies, including image recognition, self-driving cars, and products like Amazon's Alexa.

According to Fortune Business Insights, the global ML market is predicted to grow to more than $309.68 billion by 2032, up from $47.99 billion in 2025 [1]. Rapid growth in the field means there is plenty of opportunity to pursue a related career, such as in data science or ML engineering.

When you decide to start the journey into machine learning, there are three main types of machine learning you should know: supervised learning, unsupervised learning, and reinforcement learning. Below, we cover each of these different approaches in depth. Afterward, if you'd like to build foundation ML skills yourself, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization.

What is machine learning?

ML uses data and algorithms to mimic human learning, allowing machines to improve over time and become increasingly accurate when making predictions, classifying information, or uncovering data-driven insights. In its most basic form, ML works via a three-step process: (1) algorithms identify patterns contained within datasets, (2) error functions evaluate the accuracy of the algorithm's decisions, and (3) the algorithm is optimized to fit the data points and produce the best machine learning model.

Did you know?

Arthur Samuel created the term "machine learning" in reference to 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.

Read more: The History of AI: A Timeline of Artificial Intelligence

Applications of machine learning 

ML is already used around us constantly, though you may not always realize how it impacts your life. Here are a few real-world applications of machine learning in the real world that you should know:

• Social media features: Social media platforms use ML algorithms to personalize users' experiences. Apps like Instagram note 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 Assistant are all popular options if you're looking for a virtual personal assistant. These voice-activated devices can do everything from searching for flights to checking your schedule, setting alarms, and so much more. ML is a key component of these smart devices and speakers. They collect information and refine it each time you interact with them. These virtual personal assistants can then use that data to give you personalized recommendations that best match 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 personalized recommendations about products that may interest you.

• Image recognition: This complex technology is cropping up in a variety of fields. You've probably encountered this in your everyday life 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.

Hear more about the real-world applications of machine learning in this lecture from Stanford and DeepLearning.AI's Machine Learning Specialization:

3 types of machine learning  

Machine learning uses large volumes of data to learn, make predictions, find patterns, or classify information. While there are several types of machine learning, three of the most common kinds include supervised learning, unsupervised learning, and reinforcement learning. Here's what you need to know about each one.

1. Supervised learning

Supervised learning trains a machine learning algorithm on labeled datasets, effectively giving it a key to understanding how to interpret the information it's being fed. This allows the algorithm to better understand the relationship between its inputs and outputs so that it can perform the same task when eventually faced with unlabeled data or data that doesn't include tags noting "what" it is. 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.

Supervised learning examples

Supervised learning is effective for a variety of business purposes, including sales forecasting, inventory optimization, 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 learn, unsupervised learning algorithms have to figure things out by themselves (mostly). Instead of telling the machine what it's looking at, unsupervised machine learning trains the algorithm on unlabeled data, or data without tags describing what it is, so that the machine must look for patterns within the data set itself. Common algorithms used in unsupervised learning include Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models.

Considering the example from the supervised learning section above, let's say you didn't know which customers had defaulted on their loans. Using unsupervised learning, you'd provide the machine with borrower information, and it would look for patterns between borrowers before grouping them into several clusters.

Unsupervised learning 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)

What is semi-supervised learning?

Semi-supervised learning has characteristics from both supervised and unsupervised learning. In semi-supervised learning, an algorithm is first trained on a smaller set of labeled data before later being trained on a large set of unlabeled data.

Semi-supervised learning is often used to avoid the costly labeling process or when there is insufficient labeled data for a supervised learning algorithm.

3. Reinforcement learning

Reinforcement learning is the closest type of machine learning to how humans actually learn. Rather than being trained on a set of labeled or unlabeled data, reinforcement learning teaches the algorithm or agent learning by having it interact with its environment and then getting a positive or negative reward based on its decision. 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 assess 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 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 organizations 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 unlabeled data sets.

Reinforcement learning 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

Learn more about reinforcement learning in this lecture from the University of Alberta's Fundamentals of Reinforcement Learning course:

Career paths in machine learning 

The World Economic Forum's Future of Jobs Report 2025 names AI and Machine Learning Specialists among the top fastest-growing jobs [3]. In 2023, Indeed ranked machine learning engineer number eight on its list of the Best Jobs in the United States [4]. Machine learning is an in-demand field that lends itself to several possible career paths, including:

Machine learning engineer: In this role, you can work on machine learning projects and create and manage platforms. 

• Average annual salary (US): $124,352

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

• Average annual salary (US): $119,713

Natural language processing (NLP) engineer: In this role, you can work with computers, computer science, and computational language to form connections between the way humans communicate and computers understand and interpret human language. 

• Average annual salary (US): $95,171

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

• Average annual salary (US): $100,619

*Note: All salary data sourced from Glassdoor as of March 2025 and represents the average base salary for each position.

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 up for success on the path to the job you want: 

1. Earn a 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 a bachelor's degree. However, with adequate work experience and alternative credentials, those with associate degrees or high school diplomas can also get started in machine learning.

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. You can also gain experience through online courses, certification programs, and hands-on projects. Here are a few recommendations to get you started:

• Build a Machine Learning Web App with Streamlit and Python (Guided Project)

• Unsupervised Machine Learning for Customer Market Segmentation (Guided Project)

• Cervical Cancer Risk Prediction Using Machine Learning (Guided Project)

• Machine Learning Specialization by Stanford University and DeepLearning.AI

• IBM Machine Learning Professional Certificate

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.

Build your ML expertise on Coursera today

Machine learning is increasingly being woven into the fabric of our world today. Build the skills you need to join this booming field with one of these programs from industry leaders on Coursera today:

To develop practical machine learning skills, enroll in Stanford and DeepLearning.AI's Machine Learning Specialization. In this beginner-friendly program, you'll build ML models, learn best practices for ML development, and even create and train a neural network.

To prepare for a career in AI & ML engineering, consider the Microsoft AI & ML Engineering Professional Certificate. Learn how to design and implement AI and ML infrastructure, master AI and ML algorithms, and create your own AI-powered agent in this intermediate-level program.