Don't get caught not knowing the differences between the types of machine learning. Learn what they are and how you can avoid costly mistakes.

Less than 23 percent of businesses utilize any type of machine learning automation, which is surprising to hear considering the benefits that can come with it. Before deciding whether or not your business will profit from integrating machine learning, it's important to understand the differences between each type. From there, you can confidently move forward with deciding which type will offer the most value to your specific business.

In this post we'll be detailing the four primary types of machine learning and how they each function.

Unsupervised Learning

The first type of algorithm you should understand is unsupervised learning. This is when there is no form of trained data or output variables provided to the computer. Once the computer has time to collect data based on the user's patterns, it can begin to implement and teach itself things. 

Unsupervised learning essentially means that the algorithms are left to their own devises to identify and present the notable structure in a given set of data. 

Supervised Learning

A supervised learning algorithm relies on the person to train the system to recognize patterns in a data set. They will provide the computer with specific predictions and inputs, as well as the outputs of the data provided.

The user knows the correct answer while the algorithm repeatedly makes predictions on the data set and is corrected by the user when a mistake is made. Over time, the computer will begin to work and calculate according to the training that was received from the user. 

Reinforcement Learning

Reinforcement learning is a method that's used after a sizable amount of data has been collected. It takes the data that has already been retrieved, continuously learns from it, and updates the algorithm evolving overtime to ensure that it continues to remain efficient in its collection.

The user will collect a series of observations and experiences taken from the environment that it's been interacting with and continue to modify the inputs for a better output. 

This can help find a pattern when it comes to account fraud strategies that aren't successful.

Semi-Supervised Learning

Semi-supervised learning is the combination of both unsupervised and supervised machine learning algorithms. When you're looking into creating a specific model that will save your business time and money, this is the most ideal path to choose.

In the case that you have a significant amount of input data and only a small portion of it is properly labeled, this would be where a semi-supervised machine learning algorithm would be beneficial.

A good example of this would be an image archive where only some of the files are labeled, while a bulk of the files are unlabeled.

You might utilize an unsupervised learning algorithm to identify and understand the data structure of the input variables. In addition, you could use a supervised learning algorithm to make accurate predictions about the unlabeled data, feed that information directly back to the supervised learning algorithm to study, then use that model to make future predictions on new data.

Types of Machine Learning

These are the four primary types of machine learning that your business will likely need to choose between based on its specific needs. If you need more control over the training aspect, going with supervised learning may be the best option. The machine learning algorithm you utilize is dependent on the types of data sets you'll be working with and how your business expects to implement machine learning into its technical operations. 

When looking for a team to help you choose the right algorithm for a specific operational need, consider scheduling a free consultation with Vesta. Our proven solutions work for telcos, remittances, gift card, payment processors, payment enablers businesses and many others. Let them work for you too.