What Is Machine Learning? Definition and Why It’s Important

What is machine learning?

Machine learning is a type of artificial intelligence (AI) that gives machines the ability to automatically learn from big data and past human experiences to identify patterns and make predictions with minimal human intervention.

There’s more to the meaning of machine learning and how it works, which is why we’re bringing you this handy beginner’s guide! So if you want to find the answer to the question, “what is machine learning,” you’re in the right place.

Key Takeaways

• Machine learning is a type of AI that gives machines the ability to automatically learn from data and past human experiences to identify patterns and make predictions with minimal human intervention.
• Machine learning works by molding the algorithms on a training dataset to create a model. As you introduce new input data to the machine learning algorithm, it will use the developed model to make a prediction. Then the prediction will be checked for accuracy.
• There are four main machine learning methods are: supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and reinforcement machine learning.
• Machine learning can help you automate processes, save your team time, optimize your marketing and sales strategies, and much more.

Here’s an overview of everything we’ll cover:

• What is machine learning? 
• Machine learning vs. deep learning vs. neutral networks
• How does machine learning work?
• Machine learning methods and types
• The importance of machine learning
• How to choose the right machine learning model
• Machine learning use cases
• The disadvantages and challenges of machine learning

So keep reading to get all things machine learning explained!

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What is machine learning?

Machine learning is a type of artificial intelligence (AI) that gives machines the ability to automatically learn from data and past human experiences to identify patterns and make predictions with minimal human intervention.

Machine learning vs. deep learning vs. neutral networks

Now you know the machine learning definition and the answer to the question, “what is machine learning,” but how does it compare to deep learning and where do neutral networks fit in?

Deep learning and machine learning and often used interchangeably, but there have two different meanings.

Machine learning, deep learning, and neutral networks are all under the umbrella of AI. However, deep learning is under the umbrella of neutral networks and neutral networks are under the umbrella of machine learning.

Classical machine learning depends more on human intervention to learn while deep learning can use labeled datasets, also called supervised learning, to inform its algorithm, requiring less human interference.

Neutral networks are comprised of node layers that connect to each other to pass data. The “deep” in deep learning refers to the number of layers in a neutral network.

How does machine learning work?

Now that you know the answer to the meaning of machine learning and how it compares to other branches of AI, let’s explore how it works.

Machine learning works by molding the algorithms on a training dataset to create a model. As you introduce new input data to the machine learning algorithm, it will use the developed model to make a prediction.

Next, the prediction will be checked for accuracy. Based on the accuracy, the machine learning algorithm is either deployed or repeatedly trained with an augmented training dataset until it achieves the desired accuracy.

To better understand the question, “how does machine learning work,” we’ll break this process down into three steps:

1. Decision: In most cases, machine learning algorithms are used to make a prediction or classification. Your algorithm will produce an estimate about a data pattern based on some input data.
2. Error function: An error function will evaluate the prediction of the model. If there are any known examples, this function can make a comparison to evaluate the accuracy of the model.
3. Model optimization: The algorithm will adjust weights to reduce the discrepancy between the known example and the prediction repeatedly until the desired accuracy is met.

You can also learn how AI works by reading the linked article!

Machine learning methods and types

We’ve talked about the meaning of machine learning and how it works. Now let’s explore its different methods and types.

There are four main machine learning methods:

• Supervised machine learning
• Unsupervised machine learning
• Semi-supervised machine learning
• Reinforcement machine learning

Supervised machine learning

Supervised machine learning, also called supervised learning, uses labeled datasets to train algorithms accurately predict outcomes or classify data. The model will adjust its weights as input data is fed into it until it has been fitted appropriately.

An example of supervised machine learning is identifying spam emails and moving to a special classified spam folder from your inbox.

Unsupervised machine learning

Unsupervised machine learning, or unsupervised learning, uses machine learning algorithms to cluster and analyze unlabeled datasets. These types of algorithms discover hidden data groupings and patterns without human interference.

You can use unsupervised machine learning algorithms for customer segmentation, data analysis, cross-selling strategies, and more.

Semi-supervised machine learning

Semi-supervised machine learning, or semi-supervised learning, uses a smaller labeled dataset to guide classification and extraction from a larger, unlabeled dataset.

You can use this type of machine learning if you don’t have enough labeled data for a supervised learning algorithm or if it’s too time-consuming or expensive to label the right amount of data.

Reinforcement learning

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 machine learning definition, along with its different types and methods, it’s essential to understand why it matters. Here are three key advantages and benefits of machine learning.

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.

How to choose the right machine learning model

Finding it challenging to choose your model? Here are 4 handy steps for how to choose the right machine learning model:

1. Think about the problem you want to solve and which data inputs can help you find a solution.
2. Collect the data, format it, and label it if necessary.
3. Choose which algorithm to use and test it to see how it performs.
4. Fine-tune outputs until they reach the desired level of accuracy.

Machine learning use cases

Want to see machine learning technologies in action? Check out these machine learning use cases:

1. Computer vision
2. Online chatbots
3. Speech to text
4. Recommended algorithms

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!

The disadvantages and challenges of machine learning

While machine learning is certainly one of the most advanced technologies of our time, it’s not foolproof and does come with some challenges.

Here are three main disadvantages of machine learning:

It can’t attain human-level intelligence

Contrary to what some may think, machine learning is not able to reach human-level intelligence. Data is the driving force behind machines, and as a result, its “intelligence” is only as good as the data you train it with.

The models can be difficult to train

Training machines can take up a lot of time and resources. For example, large datasets are often needed to create models. You’ll also need to manually categorize those datasets, which can be tricky and time-consuming.

It’s prone to data issues

Machine learning can often lead to data issues. For example, you can experience problems with data quality, data labeling, and model confidence which can impact the machine learning process.

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Want to learn even more about machine learning?

If you want to keep learning about the meaning of 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?

It’s true! To learn more about the machine learning definition and other AI terms, and how to implement machine learning into your current marketing campaign, feel free to give us a call at 888-601-5359, or contact us online!