By CSU Global - August 10th, 2021
Recently, we explained why machine learning is so important, what machine learning specialists do, and how to launch a career in the field. Here we’re going to explore how machine learning really works.
As part of this discussion, we’ll cover what machine learning is, how it differs from artificial intelligence, the types of learning that are used in machine learning, and how machine learning solutions are being utilized by modern businesses.
After you’ve learned all about how machine learning really works, fill out our information request form to receive additional details about CSU Global’s 100% online Master’s Degree in Artificial Intelligence and Machine Learning, or if you’re ready to get started, submit your application today.
What is Machine Learning?
Machine learning is an application of artificial intelligence that helps AI systems learn and improve from experience.
Successful machine learning training makes programs or AI solutions more useful by allowing them to complete their work faster and generate more accurate results.
The process of machine learning works by forcing the system to run through its task over and over again, giving it access to larger data sets and allowing it to identify patterns in that data, all without being explicitly programmed to become “smarter.”
As the algorithm gains access to larger and more complex sets of data, the number of samples for learning increases, and the system is able to discover new patterns that help it become more efficient and more effective.
What is the Difference Between Machine Learning & AI?
As mentioned earlier, machine learning is a specific type of AI process, and the goals and scope of AI and machine learning are quite different.
While the goal of AI is to build a human-like form of intelligence, capable of solving a wide variety of complex problems, machine learning only seeks to improve an AI system’s ability for one specific task.
In this way, the scope of the two disciplines is vastly different, with AI trying to do something enormous, literally build a stand-alone replacement for human intelligence, whereas machine learning just wants to make that intelligence do a better job.
It’s easier to understand the difference here by thinking about how the fields are applied in the real world:
- AI is the technology behind Apple’s Siri, a human-like intelligent assistant capable of listening to your requests and questions, determining what you might be interested in based on the meaning behind what you’ve said, and delivering something that would be useful based on its interpretation of your intended meaning.
- Machine learning is the technology behind Amazon’s “You might also like” recommendations, which utilizes a complex algorithm to predict what products you may want to buy based on your purchase history and a comparison to other Amazon shoppers who bought similar products.
As you can see in the examples above, AI seeks to do many different things, requiring an incredibly sophisticated form of stand-alone intelligence, whereas machine learning instead aims to do one specific thing, but do it extremely well.
AI and machine learning are each incredibly useful technologies, and that’s why both fields are seeing explosive growth in use and application.
How Does Machine Learning Really Work?
The process of machine learning relies on two different types of learning, called Supervised Learning and Unsupervised Learning.
Supervised learning is a process that trains the system on known input and output data so that the system can do a better job of predicting future outputs.
To put it a little more simply, supervised learning requires that someone is in charge of providing feedback to the AI system, training the system to make the right decisions by labeling the data.
Basically, supervised learning shows the system what conclusions it should arrive at by showing it previous sets of data, and the conclusions it should have arrived at based on that data.
This helps train the system to look for data patterns, interpret those patterns, and calculate the correct answer, based on what’s worked previously.
Supervised learning is typically used when the system needs to make a prediction, like when the system is tasked with estimating house prices, or determining if a picture is a cat or a dog.
Examples of supervised learning include:
- Predicting House Pricing – The system is given a whole series of input data points, like square footage, the number of bedrooms and bathrooms, features of the house, along with an output data point, the value of the house, and the system learns to predict any new house’s price based on patterns in the previous data sets (i.e. more bedrooms means a higher price).
- Image Recognition – The system is shown pictures of cats and dogs, with labels assigned to each image so that it can learn which types of pictures and patterns in pictures represent a cat, and which represent a dog. The system can then be shown new images of cats or dogs, and use its pattern recognition “experience” whether the new image shows a picture of a cat or a dog.
Unsupervised learning is a process that trains an AI system to find hidden patterns or intrinsic structures in input data, without regard to outputs.
In this way, unsupervised learning lets the AI system draw inferences directly from data fed into the system.
Unsupervised learning is typically used for problems that require exploring data and looking for internal representations within the data, or what machine learning specialists call “clustering”.
Clustering is the process of automatically grouping together different points of data that feature similar characteristics, and assigning them to “clusters.”
Examples of unsupervised learning (really “clustering”) include:
- Customer segmentation – Identifying particular customer groups that should be targeted via different marketing strategies.
- Recommendation systems – Netflix’s suggestions for what to watch next, or Amazon’s suggestions for what to buy next, based on grouping together users that had similar viewing or purchasing patterns.
- Anomaly detection – Banks attempting to detect fraudulent financial transactions or Airlines trying to detect defects in mechanical parts.
How is Machine Learning Being Applied?
We’ve already provided several different examples of how machine learning processes can be applied to completing certain tasks, but let’s look at how this technology is impacting different industries in the modern economy.
Machine learning is finding applications virtually everywhere, but here are several illuminating examples of how the process can provide better results for businesses in different sectors:
- Transportation – Google and Tesla are both using machine learning technology to power their self-driving cars, including using deep learning processes to help the cars interpret, predict and respond to data needed to drive autonomously.
- Manufacturing – Manufacturers use machine learning to reduce process-driven losses, increase their manufacturing capacity by optimizing the production process, and reduce costs via predictive maintenance.
- Finance – Financial institutions are using machine learning technology to detect fraudulent transactions and to identify insights in financial data for purposes like advising investors about what and when to trade.
- Retail – Retail uses machine learning to power suggestions and recommendation engines (like the aforementioned Netflix recommendations, and Amazon “You might also like” suggestions), as well as to design
- Healthcare – Healthcare uses machine learning solutions to help doctors more quickly and more accurately detect the presence of certain diseases, and to detect user’s emotional states via smartphone data.
Why Should You Consider Studying Machine Learning?
The yield of machine learning is growing rapidly, with applications being found for virtually every industry, every process, and every workplace, making it an incredibly important discipline.
In fact, the International Data Corporation (IDC) reports that the AI market, “including software, hardware, and services, is forecast to grow 16.4% year over year in 2021 to $327.5 billion.”
With such rapid growth, there’s a good chance that anyone who develops their machine learning expertise will be able to develop a lifelong career in the industry.
Furthermore, jobs in this space tend to pay quite well, with U.S. Census Bureau data reporting that the average salary for AI professionals is $102,521.
If you like high technology, you want to play a pivotal role in pushing the limit of technological achievement, and you’re looking to launch a career in a growing field, there may be no better option than machine learning.
Why Should You Study Machine Learning With CSU Global?
CSU Global’s online Master’s Degree in AI and Machine Learning provides an excellent opportunity to develop your skills, knowledge, and abilities in this competitive field.
Our AI and machine learning program is regionally accredited by the Higher Learning Commission and widely respected by industry professionals, while CSU Global itself is a recognized industry leader in online education, having recently earned several important awards, including:
- A #1 ranking for Best Online AI Degree from Successful Student.
- Named to Analytics Insight’s list of Top Online Masters Courses for Artificial Intelligence in 2021.
- A #3 ranking for Best Colleges in Colorado from Best Value Schools.
- A #3 ranking for Best Value Online Graduate School from Value Colleges.
To get additional details about our fully accredited, 100% online Master’s in AI and Machine Learning program, please give us a call at 800-462-7845, or fill out our Information Request Form.
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