What Is Machine Learning?

Machine learning can be classified into different types, based on how they can learn from the data given, and also the different applications that exist. Each type has its own methods, advantages and also real-world applications.

1. Supervised Learning

In the supervised learning paradigm of machine learning, models are trained using labeled data. This indicates that input-output pairs—in which the output (label) is known—are included in every training example. By seeing patterns in the data, the model can translate the input into the output. Similar to studying with a teacher, supervised learning provides the model with clear instructions on the appropriate outputs for specific inputs.

Examples of this type include spam detection, image classification and recommendation systems. For example, when we talk about spam detection, you must have noticed how your system might itself tell you what emails look like spam. This happens by way of making use of supervised learning algorithms. By focusing on the specific features in the email content, the training data labels different emails as spam or not, allowing the models to learn different patterns that indicate these things in the best way.

2. Unsupervised Learning

As the name suggests, this technique is one that is devoid of supervision. So here, the focus is on using an unlabeled dataset that can predict output without any supervision. This aims to group the unsorted data based on the similarities and the differences in the patterns.

Take an input dataset comprising pictures of a container packed with fruit, for instance. In this case, the machine learning model is unfamiliar with the photos. The objective of the machine learning model is to recognise and classify patterns in the input images, such as colour, form, or differences, based on the dataset that is fed into it. Once the machine has been classified, it is tested using a test dataset, and its output is predicted.

3. Semi-Supervised Learning

This is a hybrid approach that combined elements of each type. This works by making use of a small amount of labeled data and a big amount of unlabeled data to ensure learning accuracy. This approach is very useful, specially when it comes to improve the accuracy of learning. In the cases when labeling data can be very time consuming, this model works best.

Medical imaging analysis is an ideal example of how this works. Because professional annotations are required, acquiring labeled data in medical imaging can be difficult. A small number of labeled medical photos combined with a large number of unlabeled ones can be used in semi-supervised learning to train models that more effectively detect abnormalities or diseases in medical scans.

4. Reinforcement Learning

This is very different from the above types. It is a feedback-based process, where AI makes use of hit and trial, learns from experiences and improves overall performance. every good action is rewarded and every wrong one is penalized. So the idea is to maximize the rewards that come from this by performing good actions.

Again, this does not have labeled data. It is mainly experience that allows learning to happen. An ideal example to help you understand this better is that if video games. In this case, the environment is defined by the game, and the reinforcement agent’s movements define its state. The agent can be rewarded or punished, which will change the agent’s overall score in the game. Getting a high score is the agent’s ultimate objective.

Both positive and negative reinforcement happens here. Positive reinforcement is when the reward is added after a behavior, whereas negative reinforcement is when the behavior is strengthened due to it avoiding a negative outcome.