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July 7, 2020

Information Technology

Machine learning: definition, explanation, and examples.

Woman scrolling through phone, machine learning

Machine learning, it’s a popular buzzword that you’ve probably heard thrown around with terms artificial intelligence or AI, but what does it really mean? If you’re interested in the future of technology or wanting to pursue a degree in IT, it’s extremely important to understand what machine learning is and how it impacts every industry and individual. And earning an IT degree is easier than ever thanks to online learning, allowing you to continue to work and fulfill your responsibilities while earning a degree.

Machine learning is the process of a computer program or system being able to learn and get smarter over time. At the very basic level, machine learning uses algorithms to find patterns and then applies the patterns moving forward. Machine learning is the process of a computer modeling human intelligence, and autonomously improving over time. Machines are able to make predictions about the future based on what they have observed and learned in the past. These machines don’t have to be explicitly programmed in order to learn and improve, they are able to apply what they have learned to get smarter. 

Machine learning vs. deep learning.

Machine learning and deep learning are extremely similar, in fact deep learning is simply a subset of machine learning. However, deep learning is much more advanced that machine learning and is more capable of self-correction. Deep learning is designed to work with much larger sets of data than machine learning, and utilizes deep neural networks (DNN) to understand the data. Deep learning involves information being input into a neural network, the larger the set of data, the larger the neural network. Each layer of the neural network has a node, and each node takes part of the information and finds the patterns and data. It then passes on what it learns to the next layer, and so on. The pieces of information all come together and the output is then delivered. These nodes learn from their information piece and from each other, able to advance their learning moving forward. Machine learning is not quite so vast and sophisticated as deep learning, and is meant for much smaller sets of data. 

Machine learning vs. artificial intelligence.

Similar to machine learning and deep learning, machine learning and artificial intelligence are closely related. Machine learning is an application of AI—artificial intelligence is the broad concept that machines and robots can carry out tasks in ways that are similar to humans, in ways that humans deem “smart.” It is the theory that computers can replicate human intelligence and “think.” There are many applications and uses of AI, and machine learning is one of them. 

Discover more about how machine learning works and see examples of how machine learning is all around us, every day. 

Feet out window of self driving car

How machine learning works.

Machine learning ultimately works by using algorithms. These algorithms are constantly searching for patterns, looking for data that can be understood and grouped together. It uses this data and the algorithms to help it learn from the past and then make guesses or predictions about the future. 

3 main types of machine learning algorithms.

There are three main types of machine learning algorithms that control how machine learning specifically works. They are supervised learning, unsupervised learning, and reinforcement learning. These three different options give similar outcomes in the end, but the journey to how they get to the outcome is different. 

Supervised learning.

In machine learning, supervised learning is fairly hands-on. It involves a human giving the machine both the input and the output. The machine uses algorithms to find out how to get from point A to point B. By giving the machine the expected outcome, you help teach it how to find that outcome in the future. It uses algorithms to find the connections between point A and point B, and is able to learn from what it is observing about both the input variable and the output variable.

Unsupervised learning.

 Unsupervised learning involves just giving the machine the input, and letting it come up with the output based on the patterns it can find. This kind of machine learning algorithm tends to have more errors, simply because you aren’t telling the program what the answer is. But unsupervised learning helps machines learn and improve based on what they observe. Algorithms in unsupervised learning are less complex, as the human intervention is less important. Machines are entrusted to do the data science work in unsupervised learning.

Reinforcement learning.

Reinforcement learning is an algorithm that helps the program understand what it is doing well. Often classified as semi-supervised learning, reinforcement learning is when a machine is told what it is doing correctly so it continues to do the same kind of work. This semi-supervised learning helps neural networks and machine learning algorithms identify when they have gotten part of the puzzle correct, encouraging them to try that same pattern or sequence again. Sometimes reinforcement learning is given an output, sometimes it is not. The real goal of reinforcement learning is to help the machine or program understand the correct path so it can replicate it later.

These algorithms often utilize specific practices to help identify patterns and organize information for the machine. Common practices include classification, regression, clustering, predictive analytics, and decision trees. The most popular ways that algorithms group information include: 

  • Classification. Classification in machine learning is where the networks will segment and separate data based on specific rules that you give them. Classifying is used in supervised training for learning algorithms. They will classify the data for you and separate it based on your specifications, so you can serve the results based on the different classes. For example, classification machine learning models can help marketers separate demographics of customers so you can serve them a unique ad based on their classification. 
  • Clustering. Clustering is similar to classifying in that it separates similar elements, but it is used in unsupervised training, so the groups are not separated based on your requirements. Clustering is commonly used in machine learning models when researchers are trying to find the differences between sets of data and learn more about them. In data analytics or data science if a researcher is trying to discover what makes certain groups different, they might try clustering to see if the computer can point out some of the subtle differences. 
  • Predictive analytics. Predictive analytics is used in machine learning models to help make determinations about the future. Based on the data a network gets, it can help make guesses about what will be in the future. Amazon is a great example of predictive analytics; based on your previous shopping experiences Amazon will show you similar items you might like based on predictive analytics. It learns from your behavior and helps give you the kinds of things you seem interested in.
  • Regression. In machine learning, regression algorithms are used to plan and model, finding the likelihood of a specific variable. Machines are able to look at different variables and forecast their connection, helping leaders understand what to expect in the future. Regression helps identify connections between data points.
  • Decision tree. Decision trees are used in machine learning as a visual way to show the decision making. Both regression and classification data can be modeled in a decision tree. Data science focuses on using decision trees to demonstrate what machine learning has found.
A man holds a tablet with graph charts and percentage points emanating out of the screen.

Machine learning examples and applications.

There are applications and examples of machine learning all around us every day. It is astounding how many businesses and companies utilize machine learning that you may not even recognize. Some examples of machine learning include:

  • Recommendations. When you watch Netflix or Hulu or when you shop on Amazon, you always get recommendations. These recommendation lists are provided by machine learning algorithms. The programs look at the things you’ve watched or shopped for in the past, and find similar options to suggest to you. It learns about you and your preferences and is able to serve you up similar items or movies for the future.

  • Social media. Similar to Netflix and Amazon, when you’re scrolling through Facebook you may get a suggestion of “people you may know.” The content on your feed and the suggestions of similar friends or events are also the product of machine learning. The program looks for patterns in your consumption habits, your friends, and events and is able to offer suggestions for the future based on what it has learned about you.

  • Online customer support. When you go on a website to ask for help you may interact with a chatbot. These chatbots utilize machine learning to read what you type and come up with similar questions or the right responses to help you. 

  • Self-driving cars. Machine learning is used in self-driving cars to help the vehicle understand what it is seeing, and react appropriately. These cars learn about traffic patterns, signs, people, and more. These vehicles are able to learn from past driving to help them be prepared for the future.

  • Smart home assistants. Smart home assistants use this learning model to create rule-based understanding. Smart home assistants take a learning problem, like understanding your voice, and use machine learning to help solve the problem. Smart home speakers are able to use machine learning to recognize your voice, to set routines, and to identify patterns in your shopping or listening. It is then able to offer you convenient, similar options in the future.

  • Healthcare. Preventative healthcare systems use machine learning to help establish care practices. Machine learning can make suggestions to providers and patients to help them, it identifies correlations and can make suggestions based on the patterns it sees. This is extremely valuable in improving patient outcomes. 

  • Language services. Language translation services rely heavily on machine learning algorithms to translate quickly and accurately. AI programs are able to look into neural networks, solve tiny pieces of the translation puzzle, and come out with an output. Prediction is a crucial element of translation services, which is made possible thanks to neural networks. Algorithms are used in translation services to help with grammar, vocabulary, and sentence structure.

As you can see, there are many applications of machine learning all around us. If you find machine learning and these algorithms interesting, there are many machine learning jobs that you can pursue. A great start to a machine learning career is a degree in computer science. This degree program will give you insight into coding and programming languages, scripting, data analytics, and more. All of this is key to many machine learning jobs. 

The importance of machine learning.

Machine learning has become an important part of our everyday lives and is used all around us. Data is key to our digital age, and machine learning helps us make sense of data and use it in ways that are valuable. Similarly, automation makes business more convenient and efficient. Machine learning makes automation happen in ways that are consumable for business leaders and IT specialists.

Machine learning is vital as data and information get more important to our way of life. Processing is expensive, and machine learning helps cut down on costs for data processing. It becomes faster and easier to analyze large, intricate data sets and get better results. Machine learning can additionally help avoid errors that can be made by humans. Machine learning allows technology to do the analyzing and learning, making our life more convenient and simple as humans. As technology continues to evolve, machine learning is used daily, making everything go more smoothly and efficiently. If you’re interested in IT, machine learning and AI are important topics that are likely to be part of your future. The more you understand machine learning, the more likely you are to be able to implement it as part of your future career.

If you're interested in a future in machine learning, the best place to start is with an online degree from WGU. An online degree allows you to continue working or fulfilling your responsibilities while you attend school, and for those hoping to go into IT this is extremely valuable. You can earn while you learn, moving up the IT ladder at your own organization or enhancing your resume while you attend school to get a degree. WGU also offers opportunities for students to earn valuable certifications along the way, boosting your resume even more, before you even graduate. Machine learning is an in-demand field and it's valuable to enhance your credentials and understanding so you can be prepared to be involved in it.

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