What Are the Different Types of Machine Learning You Can Deploy and How Do You Use Them?

Machine learning isn’t a new concept. It’s been around since the 1940s and has been used in a variety of ways. But there has been a renewed emphasis placed on machine learning as more organizations realize the different ways it can be leveraged in data and analytics—both internally and externally.

Machine learning—which is the art of using algorithms and computers to do a task better as more data is fed to the algorithm—is a primary type of data science analysis and a subset of artificial intelligence. Machine learning seeks patterns in the data provided and enables a business to use those patterns to make quicker, better decisions.

As organizations look to develop their analytics maturity and use advanced analytics such as predictive analytics, machine learning has proven to be an instrumental tool. But not all machine learning is created equal—there are different models you can deploy for different uses cases. In this blog, we will discuss the most common types of machine learning and how they work, and we’ll also cover the different types of ways you can train your machine learning models to help your business remain more agile and become more competitive, as well as best practices to get started.

What Are the Different Types of Machine Learning?

The two most common types of machine learning are supervised and unsupervised.

• Supervised learning uses labeled data—data that comes with a tag such as a name, type, or number—and guided learning to train models to classify data or to make accurate predictions. It is the simpler of the two types of machine learning, the most commonly used, and the most accurate because the learning is guided using known historical targets that you can essentially plug in to get the outcome.
• Unsupervised learning, on the other hand, uses unlabeled data—data that does not come with a tag—to make predictions. It uses artificial intelligence algorithms to identify patterns in datasets and doesn’t have any defined target variable. Unsupervised learning can perform more complex tasks than supervised learning, but it’s also less accurate in its predictions.

Both types of machine learning are used to train machine learning models. There are numerous types of models you can build in machine learning, and depending on the type of problem you have, you can use more than just one model to help make your predictions.

What Are Machine Learning Models and How Do They Work?

Machine learning models are a representation of what a system has learned. They are trained to use input data, recognize patterns using data, make a prediction based on that data, and then provide an output.

There are various types of machine learning models and some even overlap in how they work. Some different machine learning models include:

• Regression is a type of supervised learning and is used to perform regression tasks to predict a numeric value. Typical uses cases for a regression model include things like predicting sales volume or weather forecasting. Examples of questions a regression model can answer include:
  • How will stocks perform next quarter?
  • What will the temperature be in Chicago next week?
  • How much of this product will we sell this year?
• Classification is also considered to be supervised learning and works to sort data into groups. Classification models rely on existing labeled target data and related independent variables to predict which groups new data belong to. Typical uses cases for a classification model include language or pattern detection, as well as fraud detection. Examples of questions a classification model can answer include:
  • Is this flower a rose or a tulip?
  • Are our customers satisfied or dissatisfied?
  • Is this review positive or negative?
• Association rule mining is a type of unsupervised learning and is used to infer associations among data points. Association rule mining models help you understand the association between one variable and another and the relevance of that association. Typical uses cases for association rule mining models include medical diagnosis, customer behavior analysis, and user behavior on a website. Examples of questions an association rule mining model can answer include:
  • If customers purchased a bike, are they likely to purchase accessories?
  • Are customers likely to buy beer if they buy diapers?
  • What’s the probability of the occurrence of an illness given other symptoms or comorbidities?
• Clustering is most commonly used in unsupervised but can also be used in supervised learning. It is used to group similar things together, as well as to detect anomalies. Typical uses cases for the clustering machine learning model include customer segmentation, identifying behavior patterns, or detecting defects. Examples of questions a clustering model can answer include:
  • What genre of movies will this customer watch?
  • Which customers will likely purchase accessories?
  • Will this individual participate in fraudulent activity?
• Neural networks are non-linear statistical data modeling tools and are leveraged for deep learning algorithms by processing training data the same way the human brain would. Neural networks can be used for prediction, classification, association mining, or clustering. Typical use cases for neural network models include image, audio, and video recognition. But they can also be leveraged in typical business use cases. Examples of questions a neural network model can answer include:
  • What is likelihood of my electronic device failing?
  • Are my manufacturing machines functioning properly?
  • What are the issues causing a breakdown in my manufacturing processes?

How Can Machine Learning Make Your Business Agile and Competitive?

You may have great data analysts, but it’s difficult to capture the knowledge an individual might have and then be able to repeat it. It’s also difficult to capture all the possibilities that can contribute to a prediction with just the human eye. Your data analyst can look at the same three data points every week and pull that information into a regression model to predict what sales are going to look like this quarter. But what if there’s more to it? Humans are good at detecting patterns but nowhere near as good as a machine is going to be.

Machine learning allows you to learn from various features and then take that learning and make it a repeatable process. Machine learning not only helps organizations understand and anticipate customer needs and act accordingly, but it can also help analyze and improve business processes and product development, as well as fine-tune employee recruitment and retention efforts. The sky’s the limit in terms of how machine learning can be leveraged.

Understanding how each type of machine learning model works and how it can be used is a start, but ultimately you want to make your learnings a repeatable process so that you can remain agile and competitive.

What Are Best Practices to Get Started with Building Machine Learning Models?

As you look to get started with machine learning, it’s key to understand that it’s not just about getting answers to questions right now, but rather to make sure that the answers are as accurate as possible and to be able to repeat the process and allow it to evolve. It’s a long-term goal that requires some work upfront, but ultimately the results will allow your business to meet its goals and to improve, keeping pace with the market. Here are some key best practices to follow before starting with machine learning:

1.) Machine learning is part of data science. 

Before you begin to build machine learning models, you need to make sure the quality of your data is high. That involves getting your data organized, cleaned, profiled, and prepared for feature engineering. Without high quality data, you’re likely not going to get good user adoption. You’re also likely to increase the risk that you’re putting on your company and the likelihood that your data is going to be bad in the future.

2.) Don’t skip diagnostic analytics in the analytics maturity model. 

Most businesses that are doing analytics are doing descriptive analytics. From there, they jump to predictive analytics, which is where machine learning comes into play. But it’s that step in between—diagnostic analytics—that sometimes is overlooked. Diagnostic analytics helps you understand the “why” behind your data which helps to build better machine learning models because you already know what’s going into it and it makes them much more interpretable.

3.) Leverage business intelligence (BI) tools to enhance interpretability and communication.

While you can design visuals using python or R, sharing content with end users can be tricky. We find that best practices involve utilizing existing BI tools—such as Power BI, Tableau, or Qlik Sense—helps with communicating the results of the machine learning. Including how and why a model works in a visual also helps with interpretability and avoids data science in a “black box”. Additionally, using familiar tools, which are highly interactive, allows for easier access, wider adoption, or even discovery of new use cases.

4.) Make sure to consider model monitoring after operationalizing machine learning into your processes. 

Models can incur data drift—which is change in model input data that leads to model performance degradation—over time, potentially providing wrong answers or making the model irrelevant. Inclusion of a plan to monitor the model and maintain the inputs, as well as revisit what you’re targeting, can help ensure continued quality and success of any machine learning effort.

5.) Staff accordingly.

Data science is a team learning sport. Not only do you need data scientists, but don’t forget to include data engineers, analysts, and subject matter experts in your staffing plans. Having the right people involved can make all the difference. And if you need to supplement data scientists, there is a lot of software out there to help with autoML, such as DataRobot.