A Short Guide to Machine Learning: Types and Applications

Today, machine learning and its applications have taken over virtually every sphere of our lives. From smartphones to healthcare to complex industrial systems, AI-powered algorithms amplify the capabilities of existing devices and allow us to process data at unimaginable speed.

Global companies apply machine learning to improve business performance, adapt to evolving markets, and predict future trends. However, machine learning comes in different flavors. Therefore, it’s essential to know the main types of ML to make the most of them.

Source: Medium

Under the hood of machine intelligence

Powered by artificial intelligence, machine learning is what gives computers the ability to dig up insights into data. Based on these insights, a smart system can also generate predictions and uncover hidden patterns.

Technology has become a cornerstone in a variety of fields. From medicine to finance to marketing, this AI offshoot is put at the core of groundbreaking applications. It is also becoming increasingly important as we collect more and more data. As data looms large, we need ways to make sense of it all, and machine learning is one of the best ways to do that.

Due to the pandemic-induced demand for automation, companies have accelerated their adoption of the technology. According to Globe Newswire, the market size of ML is projected to rise from over $21 billion in 2022 to $209 billion by 2029.

Why adopt machine learning in your next project?

Now let’s see how different types of ML algorithms can fuel hundreds of innovative applications.

Cost savings

By using machines instead of humans for certain tasks, you can save money and reduce labor costs. For example, instead of hiring a salesperson who needs to know about every product in your inventory, you can use an algorithm that learns from past sales patterns and predicts which products will sell better in the future.

Better decision-making

Without AI-based systems, companies are unable to process large chunks of data they get every day from transactions, touchpoints, and others. As a result, executives rely on guesswork for their decisions, which increases operational risks. To avoid that, business leaders rely on intelligent systems that do all the heavy data lifting and return accurate and actionable results.


Artificial intelligence is touted to be an automation enabler and for a good reason. It allows companies to use the baked-in knowledge of data scientists without spending time and resources on hiring the whole IT department.

Improved customer experience

Last but not least, the built-in intelligence amplifies customer experience. It helps sales and marketing teams to fine-tune their offerings and deliver granular initiatives at the right time and to the right cohort of customers.

Stop#1: Supervised type

Supervised learning is a subfield of machine learning that bases its capabilities on tagged input. You can think of it as a school or class in which you have a set of training data with both input and output variables. The goal of this learning method is to learn the mapping between the inputs and outputs and thus, by extension, to predict new values for those output variables.

We often use this type of classification for predicting things like user behavior or stock prices based on previously observed values. Other common tasks of supervised learning include:

  • Fraud detection in banking
  • Spam detection
  • Object recognition, and others.

The most common way to implement these machine learning models is through regression analysis.

Stop#2: Unsupervised type

Unsupervised learning, on the contrary, doesn’t need tagged stimulus. In other words, it refers to a set of algorithms used to analyze data without being given any labels or support for target classifications.

This type of ML can be used as a standalone algorithm or in combination with supervised learning, where labeled data are used as additional training information. Unsupervised algorithms help us discover hidden patterns within the dataset and make sense of it by looking at relationships between different variables.

As this cohort of algorithms can cluster unlabeled datasets, the common applications of this learning type include:

  • Finding customer segments
  • Feature selection (credit risk score)
  • Pattern detection, and others.

Stop#3: Semi-supervised type

As the name implies, semi-supervised machine learning is premised on both types of input – both tagged and untagged. This can be useful when you have a lot of data that is not labeled, as it can help you to learn from that data and improve the accuracy of your machine learning models.

To execute this ML type, you need to have a way to label some data so that the machine learning algorithm can learn from it. This can be done in a number of ways, but one common method is to use a technique called self-training.

With self-training, you label a small amount of data and then use that data to train the machine learning algorithm. The algorithm is then used to label the remaining data, which is then used to improve the accuracy of the algorithm.

The application matrix of this type of algorithm includes the classification of text documents, speech analysis, web content classification, image analysis, and others.

Stop#4: Reinforcement type

Reinforcement learning is different from those three and returns results based on rewarding desired behaviors and/or punishing undesired ones. This type of learning is similar to how we humans learn – by trying things and receiving feedback. Agents in reinforcement learning use trial and error to figure out the best actions to take to receive the most rewards.

Reinforcement learning is distinct from other types of learning because the agent is not given explicit instructions on what to do. Instead, the agent must learn from experience and trial and error. This type of learning is often used in artificial intelligence and machine learning applications, including autonomous vehicles, game optimization, and others.

The Final Word

Machine learning is a major area of computer science research and development, with applications in many areas including computer vision, speech recognition, natural language processing, and machine translation.

The technology has become omnipresent in a wide variety of domains, from data mining and search engines to robotic process automation. Many real-world systems use machine learning algorithms to provide features for automatic content generation, categorize objects in images, and automatically diagnose diseases.

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