What are the different types of machine learning?
Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic types of machine learning: supervised learning, unsupervised learning, semisupervised learning and reinforcement learning.
The type of algorithm data scientists choose depends on the nature of the data. Many of the algorithms and techniques aren't limited to just one of the primary ML types listed here. They're often adapted to multiple types, depending on the problem to be solved and the data set. For instance, deep learning algorithms such as convolutional neural networks and recurrent neural networks are used in supervised, unsupervised and reinforcement learning tasks, based on the specific problem and availability of data.
How does supervised machine learning work?
In supervised learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and output of the algorithm are specified in supervised learning. Initially, most machine learning algorithms worked with supervised learning, but unsupervised approaches are becoming popular.
Supervised learning algorithms are used for several tasks, including the following:
- Binary classification. Divides data into two categories.
- Multiclass classification. Chooses between more than two types of answers.
- Ensembling. Combines the predictions of multiple ML models to produce a more accurate prediction.
- Regression modeling. Predicts continuous values based on relationships within data.
How does unsupervised machine learning work?
Unsupervised machine learning algorithms don't require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms.
Unsupervised learning algorithms are good for the following tasks:
How does semisupervised learning work?
Semisupervised learning works by feeding a small amount of labeled training data to an algorithm. From this data, the algorithm learns the dimensions of the data set, which it can then apply to new unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time-consuming and expensive. This type of machine learning strikes a balance between the superior performance of supervised learning and the efficiency of unsupervised learning.
Semisupervised learning can be used in the following areas, among others:
- Machine translation. Teaches algorithms to translate language based on less than a full dictionary of words.
- Fraud detection. Identifies cases of fraud when there are only a few positive examples.
- Labeling data. Algorithms trained on small data sets learn to apply data labels to larger sets automatically.
How does reinforcement learning work?
Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. A data scientist will also program the algorithm to seek positive rewards for performing an action that's beneficial to achieving its ultimate goal and to avoid punishments for performing an action that moves it farther away from its goal.
Reinforcement learning is often used in the following areas:
- Robotics. Robots learn to perform tasks in the physical world.
- Video gameplay. Teaches bots to play video games.
- Resource management. Helps enterprises plan allocation of resources.
