Thankfully, there are only two types of machine learning: supervised and unsupervised. If you’re wondering about the difference between them and how they work, this article should help clear things up.
Supervised Learning is a machine learning technique that uses labeled data to train the model. It is used to predict an output variable given some input variables, in other words, you have training data which contains both inputs and corresponding outputs.
The model learns from the training data and make predictions on new data.
What is Supervised Learning?
Supervised learning is a type of machine learning in which the training data has associated labels. The model learns from this labeled data, and then applies its knowledge to new observations to make predictions about the future.
Supervised learning can be used for many tasks including predicting values for continuous variables (such as sales) or categorical variables (such as product category). In addition, supervised learning models can also be used as classifiers that predict if an observation belongs to one class or another (e.g., spam vs non-spam emails).
How Does Supervised Machine Learning Work?
Supervised machine learning is a machine learning technique that uses labeled data to train models. The model learns to make predictions based on the input data, and these predictions are then compared with the actual values of the output variable in each example. The difference between what was predicted and what actually happened is used to update the model so that it can be improved for future use.
In contrast, unsupervised learning does not require any labels or targets; instead it searches for patterns within the data itself without using prior knowledge about what those patterns should look like (e.g., finding clusters).
You should now have a good understanding of what supervised learning is, as well as how it works.
Congratulations, you now have a good understanding of what supervised learning is and how it works! You should be able to answer the following questions:
- What is supervised learning?
- How does supervised learning work?
- How can I use supervised learning in my own projects?
Supervised learning is an important machine learning technique that can be used to solve a wide range of problems. It allows us to build models that make predictions based on known information, which is why it’s sometimes referred to as “predictive modeling.” This type of learning requires labeled data–that is, information about what should happen in certain situations if there is no uncertainty (such as when we know exactly how much money will be spent on groceries each week).