Learning models
Now that we have a basic understanding of ML and how to train a model, let's have a look at three types of ML learning. These are as follows:
- Supervised learning
- Unsupervised learning
- Semi-supervised ML
The preceding three types of ML are defined as follows:
- Supervised learning: Supervised learning is the most common model. It is used when the training data and validation data are labeled. What the model does is learn how to set a label for input data. It does this based on what it has learned from some labeled training data. We can further classify supervised learning into the following categories:
- Classification: This occurs when the output data is a category, such as apple, pear, or orange.
- Regression: This occurs when the output data is a value, such as cost or temperature.
- Unsupervised learning: Unsupervised learning is used when the training data is not labeled. The model attempts to learn the structure of the data and export...
