What Are the Types of Machine Learning? (Plus When To Use Them)

Machine Learning has found its applications in almost every business sector. There are several algorithms used in machine learning that help you build complex models. Each of these algorithms in machine learning can be classified into a certain category. In this article, we’ll learn about the types of machine learning. This will give you better insight into the field of machine learning.

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Why are there multiple types of machine learning?

There are many types of machine learning to help solve various problems and meet different sets of objectives. Each has its own advantages and disadvantages. When choosing a method for machine learning, individuals consider what they want the machine to do. Another consideration is whether they have access to label data. If so, the individual may select either supervised or semi-supervised learning. However, if someone is interested in looking at patterns and new structures, they may consider using unsupervised learning.

What is machine learning?

Machine learning is a type of computer science and artificial intelligence (AI) that uses data and algorithms to copy the way humans learn. Through the use of data and experiences, machines can make predictions and recommendations. Some common uses of machine learning include providing online recommendations, filtering spam emails and giving web search results. Machine learning uses algorithms, developed by finding patterns, to recreate a model and produce a desired output.

4 types of machine learning

Heres a list of the different types of machine learning:

1. Supervised learning

Supervised learning is when a machine uses data and feedback from humans about a case to help it produce the desired outcome. For instance, a company may show the machine 500 images of a stop sign and 500 images that are not a stop sign. In this scenario, the stop sign and not a stop sign are the outcome and become the labeled data. Under the supervision of the labeled data, the machine learns about the relationship of the stop sign. This allows it to classify whether or not an image is a stop sign.

Supervised learning is task driven and can be helpful in predicting the next value in a model. Common supervised learning algorithms include:

2. Unsupervised learning

In unsupervised learning, the machine lacks assistance from the user. Instead, it finds patterns in data that humans may have missed and discovers unknown results. Unlike supervised learning, unsupervised learning uses unlabeled data for data points. Through using these data points, the machine makes references to discover meaningful patterns and structures. Unsupervised learning is data driven and focuses on finding clusters. Some unsupervised learning algorithms include:

3. Reinforcement learning

Reinforcement learning uses a trial-and-error method to improve and learn from new situations. To reinforce and maximize favorable actions, it uses a reward system that sends a positive signal for good behavior. This type of learning is behavior driven.

In order to use reinforcement learning, you must have an agent and an environment, with the goal being to connect the two using a feedback loop. For instance, if you want your machine to complete a maze, the agent would be the learning algorithm and the environment would be the maze. Reinforcement learning algorithms include:

4. Semi-supervised learning

Semi-supervised learning uses a limited set of labeled data to train itself in shaping the requirements of an operation. This learning combines a small amount of labeled data with a large volume of unlabeled data, using both supervised and unsupervised learning. It can be a cost-saving method since it involves only using a limited amount of labeled data.

To use this type of learning, train the machine with a small amount of labeled data. You then give it an unlabeled dataset to predict the outputs. These outputs are pseudo labels since they may be inaccurate. Once you have your pseudo labels, link them with the labeled data. You also link the data inputs from the labeled data with the inputs in the unlabeled data. Finally, train the model with the label data to minimize errors and improve the models accuracy. Some semi-supervised learning algorithms include:

When do businesses use each type of machine learning?

Businesses use different types of machine learning depending on their end goals. The following are some examples of when a company might use each type of machine learning:

Supervised learning

A company may use supervised learning for the following purposes:

Unsupervised learning

Unsupervised learning helps companies to perform the following tasks:

Reinforcement learning

Businesses may use reinforcement learning when trying to reach the following goals:

Semi-supervised learning

Semi-supervised learning helps businesses complete tasks such as:


What are the 4 types of machine learning?

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What are the 3 types of machine learning?

There are three machine learning types: supervised, unsupervised, and reinforcement learning.

What are the main 3 types of ML models?

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

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