…And how does it work?
When we say machine learning. You say, ‘That has something to do with AI, right?’ And you’re correct! Don’t worry about not knowing the exact definition of machine learning. A lot of people have to look it up! But luckily we’re here to explain it to you. So, let’s get to the bottom of what machine learning is.
Machine learning is a form of artificial intelligence that teaches computers to learn from data and to get better through experience, so you don’t have to keep reprogramming them with improvements. It is about training algorithms to find patterns in data and to use this analysis to make the best decisions and predictions.
Machine learning is used a lot in everyday life – in our homes, our shopping carts, our entertainment media, and in health care.
The difference between AI and machine learning
Machine learning and AI are often named in the same sentence. But they are not quite the same thing. Machine learning is a form of AI. So, machine learning is always AI. But AI is not always machine learning. AI is the umbrella term, and machine learning is just one of the many things under that umbrella.
Deep learning and machine learning are also often confused with each other. Deep learning is a subcategory of machine learning models. It attempts to imitate the functioning of the human brain and is used for speech recognition, computer translations, and facial recognition. “Deep” here refers to the number of layers in the neural network.
How does machine learning work?
Machine learning consists of different models using different algorithm techniques. Algorithms for machine learning are designed to classify things, find patterns, predict results, and make informed decisions. Depending on the data and the desired result, you can opt for one of four different models:
- supervised learning
- unsupervised learning
- semi-supervised learning
- reinforcement learning
The difference between supervised and unsupervised learning
Let’s have a quick look at the difference between two of the four machine learning models:
- Supervised machine learning
In supervised machine learning, a data scientist serves as an intermediary who teaches the algorithm what conclusions to draw. The algorithm is trained using a pre-labeled dataset. You can compare this to how you might teach a kid the different kinds of fruits using pictures and naming them. Once the teaching period is over, the computer (or kid) knows the difference between an apple and a pear.
- Unsupervised machine learning
In unsupervised machine learning, the computer gets the hang of complex processes and identifying patterns without the guidance of humans. Training is done using unstructured data without labels. If we go back to our kids and fruits analogy, the kid would learn by recognizing colors and observing patterns, rather than through us showing them pictures of the fruit and telling them the names.
What can we use machine learning for?
We can use machine learning to predict, discover and detect. These skills are useful for a wide range of things. For example, you can use machine learning to:
- Predict what customers are likely to buy.
- Detect mistakes while working on written content and receive suggestions for improvements.
- Discover the best possible moment to publish a social media post based on data.
The second item on the list is how Textmetrics uses machine learning. Enter content in the text optimizer on our platform for text improvement, and it will give you suggestions for improving the quality of the text. This makes writing easier and more efficient. You’ll write understandable, high-quality, and gender-neutral content that appeals to your entire target group.
Want to read even more about AI, our platform and more? Check out our other articles here.
