3 Types of Machine Learning

Machine learning (ML) is finding its application across many industries and established business practices. It’s a subset of artificial intelligence solutions that are adopted by businesses and organizations everywhere.

ML focuses on gaining knowledge from large amounts of data. There are many different ways ML models approach data analysis. Specific applications require unique approaches to learning, so there are a few different types of ML models. This article will help you understand the differences between ML models and learn how they help improve operations in practice.

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Machine Learning Explained

Machine learning is the process of teaching computers to improve by analyzing large amounts of data. The process uses advanced math models called algorithms to segment data into groups and find correlations between them. The goal is to create a computer model that improves decision-making over time. 

With that said, there are multiple types of ML models. Each of them is designed for specific uses, and they use different approaches to learning. The three main types of ML models are supervised, unsupervised, and reinforcement machine learning. 

AI development largely depends on using the right ML model to ensure high-quality results. However, the AI itself can’t figure out which model fits the application best, so a human expert is needed to identify the best option. People are also responsible for creating the right learning environment. Once the model starts doing what it’s supposed to do, data scientists can understand what made them work. Let’s go over all three ML model types in more detail.

Supervised Machine Learning

Supervised ML models are by far the most popular type of all three. These models are the fastest to learn and the easiest to implement in existing operations. Imagine teaching a child using flashcards. Supervised ML models use a similar method to understand data and learn from it. 

These types of ML models can only read labeled data. As it filters data, it pairs similar information, allowing the algorithm to label every group and every example. It then provides feedback to see if the prediction was correct. After analyzing data over a longer period, the algorithm becomes more accurate and can learn about the relationships between labels and examples. In the end, the ML model can make accurate predictions when fed with unknown unlabeled examples by applying the same logic. 

That’s why supervised ML models are also known as task-oriented ML. These types of ML models can only complete one task, and the more information they analyze, the better they become at completing the same action. Here are some of the popular applications of supervised machine learning systems:

1. Face Recognition 

Facebook has been using face recognition technology for over a decade. Whenever a user uploads a photo, Facebook’s supervised ML algorithm recognizes every face and suggests who to tag. The process is much more complex than it may seem. The ML model has to find faces, identify them, and provide tagging suggestions. It’s extremely accurate due to the billions of photos it has already analyzed.

2. Spam Classification

Most popular email providers use ML as a spam filter. The ML model analyzes millions of emails to learn how to label all emails accurately. After some time, these filters can identify malicious emails and prevent them from ever reaching the user. Moreover, advanced ML spam filters can create new labels to improve the filtering process according to every individual user.

3. Advertisement Reach

Companies far and wide use supervised ML models to find the best-performing ads. Most of the ads you run into when surfing online are displayed because an algorithm identified them as popular. Ads you see on certain sites when using a search engine are also a direct result of an algorithm finding the best place for the ad in question. 

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Unsupervised Machine Learning

As you can probably imagine, unsupervised learning is the exact opposite of supervised learning. It doesn’t care about labels and data types. Unsupervised machine learning uses different algorithms to analyze data and recognize patterns. These types of ML models develop their methods for grouping and organizing data in a way that makes it easy for humans to read. 

Its ability to analyze unlabeled data is truly impressive. Advanced algorithms can analyze massive amounts of unlabeled data and structure it in a way that helps industries improve their operations. It’s a data-driven learning method, as all outcomes are controlled by the data. Here are some applications of unsupervised machine learning:

1. User Log Grouping

User log grouping is an important process that allows companies to identify the main issues their customers face. The goal is to find key information that can help improve a product, design, or simply create a better knowledge base where users can find answers. It’s an active process that needs unsupervised learning algorithms to cluster issues and provide answers.

2. Purchasing Habits

Whether you’re aware of it or not, all of your buying habits are stored in a database somewhere on the internet. Companies and businesses then buy that data to get insights into their customer’s buying habits, so they can improve offers and increase sales. 

3. Recommender System

Video streaming platforms such as YouTube and Netflix both have a video recommendation system. These systems are unsupervised, and they track how users interact with content. They can tell what type of videos people prefer, how long they spend watching them, what genres they prefer etc. The system then suggests other similar videos the user might like.

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Reinforcement Learning

Reinforcement learning is completely different from the first two types we covered above. It doesn’t look for labels, and it doesn’t create them either. Instead, it’s a type of system that learns from mistakes. When you place this system in an environment, it will make a ton of mistakes at first. However, as long as it gets a signal that allows it to tell the difference between failure and success, the system can reinforce its algorithm and make fewer mistakes over time. 

It’s a behavior-driven model influenced by neuroscience and psychology. It’s widely used in the video game industry, resource management, and many other areas. The model interacts with all possible options and reinforces positive actions. Here are some applications of this type of system:

1. Industrial Simulation

There are two ways of using robots in production. You can either hardcode processes to make them do what you need them to, or you can let them figure it out on their own. The latter option is cheaper and safer, and it’s less prone to failure. 

Running simulations will help the model find the best way to get things done before testing things in the real world. The Wizata industrial manufacturing software comes with digital twin technology that is able to run the most accurate industrial simulations ever.

2. Resource Management

Reinforced learning can help reduce costs by balancing resource requirements. For example, it can help increase power consumption efficiency, reduce data storage costs, etc.

3. Video Games

Video games use reinforced learning ML models to test out the game design. These models can learn how to play a video game just like a real person, which helps improve the game’s AI and the overall user experience.

Final Words

These are the three primary types of machine learning models that are already redefining entire industries and business practices. They are very different, but they provide similar results that are usually an increase in efficiency and productivity, and cost reduction.

In total, there are over 14 different ML models, but we didn’t get into the details because the other 11 are not specific enough. We hope that this article helped you understand the differences between machine learning types and their applications. 

Author bio

Christopher is a digital marketing specialist at Digital Strategy One and a freelance blogger. He is focused on new web tech trends and digital voice distribution across different channels. In his free time Christopher plays drums and Magic: the Gathering. 

Jennifer Alex

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