Machine learning is becoming increasingly relevant in a wide variety of industries, but do you find yourself asking, “What is machine learning?” If you’re not sure how to define machine learning, you’re in the right place. On this page, we’ll give a machine learning intro, talk about machine learning algorithms, and answer your burning question, “What is machine learning?” If you’d like to learn more, keep reading. If you’d like to speak with a specialist about machine learning, feel free to contact us at 888-601-5359!
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Intro to machine learning
There’s a lot to unpack in our machine learning intro.
What is the definition of machine learning? How does it work? Is machine learning the same as artificial intelligence?
What about the definition of deep learning? Let’s unpack this baggage one by one with a machine learning introduction.
What is machine learning?
Machine learning, by definition, is the science of getting computers to accomplish specific tasks without programming them to do so directly. Instead, computers learn how to do things from the data provided by the programmer.
Computers take that information and learn from it, which takes the place of physically programming the computer. For example, if you continuously feed the computer math problems like 2+3=5, 5×5=25, and so on, the computer will learn how to do simple math with the use of machine learning. Computers will begin generalizing based on information provided to them, which, in the long run, can automate countless processes.
Is machine learning the same as artificial intelligence?
When it comes to comparing machine learning, artificial intelligence, and deep learning, it’s easier to understand than you think.
Many people believe that all three are the same thing, but there are subtle differences that make them completely different. Here’s how it works: Think of a peach — it has the pit on the inside, the juicy fruit layer, and the fuzzy skin. Deep learning would be the pit — it is part of the peach and is a specific kind of machine learning.
Machine learning, then, would be considered the juicy fruit layer of the peach. Machine learning is a category of artificial intelligence, and a kind of machine learning would be deep learning. The fuzzy outside layer, then, is artificial intelligence, which is an umbrella term for the smart, intuitive things that computers can do — one of them being machine learning.
Let’s define each:
Definition of deep learning: According to Forbes, deep learning is a “subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.”
Definition of machine learning: This refers to computers learning from large sets of data and being able to complete human tasks without being programmed to do so, but instead learning from the data provided to them.
Definition of artificial intelligence: The way that computers mimic humans in what they can accomplish by looking at large sets of data.
What are the different machine learning algorithms?
When you ask yourself, “What is machine learning?” it’s likely that you also ask yourself some more in-depth questions like, “What are the different methods of machine learning?” Here are some different ways that you can teach your computer the ways of your business.
When you opt for supervised learning, it means that you train your computer with questions that already have an answer. For example, with supervised learning, you would give your computer the following list of information:
The computer will learn from seeing both the issue and the resolution.
As you may have guessed, unsupervised learning is when you provide your computer with questions that don’t have an answer. The goal is for the algorithm to figure out the right answer on its own and apply that to future data.
With unsupervised learning, you would give your computer the following list of information:
Semi-supervised learning is when you provide your computer with:
- Problems with answers
With semi-supervised learning, you would give your computer the following list of information:
Reinforcement learning is nothing more than your computer using trial and error to figure out what answer is correct by determining what results provide the best reward. The goal is for your computer to learn what problem resolutions provide the best outcome for the user.
Three reasons machine learning is important
Now that you know the background of machine learning, it’s essential to understand why it matters. There are a few reasons why machine learning is one of the most significant advances of our time.
1. There is a lot of data floating around
Whether you plan to use machine learning to better your marketing strategy or want to take advantage of it in another area of your business, it’s useful to every industry. But why can virtually every industry benefit from machine learning? Simple — there is so much data available that you can use to better your company.
Chances are, you have spreadsheets upon spreadsheets of data and information that you don’t even know how to use. Why not put that data to good use and train a computer to do some work for you? Not only that, but machine learning is a great way to store your data as well.
2. It automates processes
If you own a business, you likely utter the words, “I’m too busy,” more than once every day. With machine learning, you can automate processes that you typically spend hours doing. Of course, it takes time to train your software to become proficient in your industry’s machine learning algorithms, but once you do, you’ll be able to automate a wide variety of actions.
3. You can create a better business with machine learning
So far, we’ve talked about nothing but the benefits of machine learning, and we’re about to talk about a third. You can virtually create a better business with machine learning for a wide variety of reasons. Not only does machine learning free up your time and let you work on other high-priority items, but it also allows you to accomplish things that you never thought were possible.
For example, if you choose to use machine learning for your marketing campaign, you can train a chatbot to help clients find the answers they’re looking for. Not only does this free up your time, but it gives users another way to contact you and learn about your services.
Examples of machine learning
- Computer vision: This allows a computer to understand meaningful information through images, videos, and other visual aspects. Based on what the computer finds, it can then take action and make recommendations of courses of action. Technology like this can be found in applications related to social media, healthcare settings, and self-driving cars.
- Online chatbots: These online areas to chat are frequently on the website, where a user can quickly ask a question if needed. This machine learning involves the computer answering frequently asked questions (FAQs) and providing advice based on that. These virtual agents can be helpful to steer one in the right direction and give any business employee a break.
- Speech to text: Yes talking to your phone is using machine learning! This is where a computer uses its processing to understand and interpret what we say into text form.
Siri is a popular example!
- Recommendation algorithms: This kind of machine learning is something that is very important to the functions of digital marketing today. A recommendation engine uses algorithms to learn from past data, to make effective decisions on what to do next. This kind of data is great to have to understand what is working, and what might not be. Also, this engine helps to create more streamlined, and effective strategies for your business!
Want to learn even more about machine learning?
If you want to keep learning beyond our intro to machine learning, WebFX can help. Did you know that we even have proprietary software called MarketingCloudFX that utilizes machine learning to provide our clients with the best possible results?