Artificial intelligence is the key to solving problems that are too difficult for humans to solve.
Supervised learning is the most common approach in AI, and it is what allows computers to learn how to do things like recognize objects or make predictions about future events.
Here, we'll take a look at what supervised learning really is and why it's such an important part of artificial intelligence.
What is supervised learning?
Supervised learning is a type of machine learning algorithm that helps computers learn to do things, like recognize objects or make predictions about future events.
In supervised learning, the computer learns from examples in which the correct answer is known.
For example, let's say you're trying to teach a computer how to play chess.
To do this, you might show it a chess board with an opponent on each side and tell the computer what moves to make. The computer would need to figure out how those moves work in order to win the game, and supervised learning could help it do so.
The main goal of supervised learning is to enable machines to "learn" new things by observing behavior and carrying out actions with limited input from humans.
The machine will then be able to carry out these tasks without human guidance, which will save time and money for businesses and consumers alike who rely on computers for information processing.
How does supervised learning work?
Supervised learning is a form of machine learning that uses an algorithm to identify patterns in data, then learn from these patterns. The algorithm takes any number of inputs and outputs them into a set of predefined classes.
The supervised learning algorithm starts by randomly assigning each input to one of the classes.
Then, it looks for patterns in the inputs and outputs, and compares the two sets to find relationships between them. If there's a pattern, the algorithm will adjust its behavior so as to fit this new pattern.
This process continues until the algorithm finds that it has learned enough about how things work in order for it to be able to do what's asked without having to receive feedback from humans anymore.
As long as you can provide enough labeled data, supervised learning can figure out on its own how to operate however you want your computer or robot to operate.
Data labeling and supervised learning
In order to train AI models with supervised learning, you need to have labeled data. This is the information in which you've already identified what's important and what isn't.
Machine learning algorithms are trained using labeled data as input, but in order to ensure accuracy, it's important to label your data correctly.
The most common way of labeling your training data is by creating a binary classification system. A binary classification system consists of two labels: one for each outcome you're trying to predict.
For example, if your goal was to predict whether or not someone would make it through the program, you might label people that made it through the program as "successful."
On the other end, people who didn't make it through would be classified as "failed." All of these classes could be used for supervised learning models because they all include an outcome that your algorithm needs to predict.
The next step in labeling your data is assigning values to each class. With binary classification systems, these values are often represented by 0s and 1s. Successful individuals are assigned one value (1), while failed individuals are assigned another value (0).
What is the difference between supervised learning and unsupervised learning?
Supervised learning is used to train a computer program on how to do something by showing it examples of the desired behavior.
Unsupervised learning, on the other hand, is when a computer program analyzes data without specific instructions from humans to determine what it should do.
This type of learning includes any kind of AI that has been preprogrammed with a model or set of instructions in order to learn how to perform an action or solve a problem.
This process can be done manually by humans, but it's much more efficient for computers because they can automatically analyze large amounts of data in order to find patterns, which allows them to learn quickly.
For example, supervised learning could be used to identify patterns in a customer's purchase history so that your company can make better quality recommendations based on past purchases.
Unsupervised learning is a little different.
In this case, an AI program would look at all the data available and analyze it without any explicit guidance from humans.
This type of learning is usually used for things like natural language processing (NLP) and machine vision where you don't know what the correct answer will be ahead of time.
What is an example of supervised learning?
Image recognition is one of the best-known applications of supervised learning.
When a computer is asked to identify a specific object in an image, it first sees all the objects in the image and then looks for patterns that match what it has seen before. The computer will then "learn" what these patterns are and how to recognize them in new images.
For instance, if you asked a computer to identify a cat, it would first see all cats in your photo and register them as similar. It would then look for smaller shapes like ears or paws and learn that this is what distinguishes a cat from other animals. At this point, it would start recognizing cats without any help from you!
But there's more to supervised learning than just identifying objects! Many important tasks in AI rely on supervised learning methods as well: including predicting whether someone will like an article or recommend it to others or deciding which ad to show when someone searches for something on Google.
What are some supervised learning algorithms?
Supervised learning algorithms are used in supervised learning methods, which are the most common approach to AI.
In supervised learning methods, a computer program is given a set of inputs and an outcome. The computer program analyzes those inputs and outputs in order to predict future outcomes.
For example, a computer program could be given a set of images of dogs and asked to identify which one is a dog. With this type of algorithm, the computer program would analyze the images provided and determine whether or not they're dogs.
The goal of supervised learning algorithms is to figure out what actions will allow you to achieve your desired outcome. After that, they would need to learn how to do the actions so they can continue to accomplish their goal.
There are many different types of supervised learning algorithms including linear regression, logistic regression, support vector machines (SVM), and decision trees (which are used for classification). These algorithms work best when there's sufficient data available for them to analyze.
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As Chief Content Officer, Mike Kaput uses content marketing, marketing strategy, and marketing technology to grow and scale traffic, leads, and revenue for Marketing AI Institute. Mike is the co-author of Marketing Artificial Intelligence: AI, Marketing and the Future of Business (Matt Holt Books, 2022). See Mike's full bio.