Introduction
Machine learning, like any other type of learning, is not a single entity. There are different ways in which it can be done and each one requires a different approach and skillset. In this blog post, I’ll explore some of the common types of machine learning algorithms (supervised, unsupervised, semi-supervised) to help you better understand how they work in practice and which ones would be best suited for your needs
Supervised Machine Learning
Supervised machine learning is a type of machine learning where the algorithm is trained with labeled data to predict outcomes. Supervised learning can be used for classification and regression problems, but it’s most commonly used in situations where there are known labels or categories for the input variables.
The goal of supervised ML is to find an optimal function that predicts the target variable based on its inputs (or features). In this way, supervised ML allows you to make predictions about new data points based on what has already been observed and classified by humans or other algorithms in your system.
Unsupervised Machine Learning
Unsupervised machine learning is used to discover hidden patterns in data. Unsupervised learning is also used to learn from unlabeled data, which means it does not require any labeled examples for training. Examples of unsupervised learning include clustering, anomaly detection and dimensionality reduction.
Unsupervised machine learning can be used on its own or in combination with supervised techniques like classification or regression problems that we will explore next week!
Semi-Supervised Machine Learning
Semi-supervised machine learning is a subset of supervised machine learning. It refers to the use of semisupervised data or unlabeled data in addition to labeled data for training an algorithm. In practice, semi-supervised algorithms can be used when there are only a small number of labeled examples (e.g., <20{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af}), but you want your model to generalize well on unseen test instances.
Semi-supervised learning algorithms improve the performance of supervised machine learning algorithms by adding information from unlabeled examples into the training process, which can help prevent overfitting and improve generalization ability.
All machine learning algorithms are not the same.
All machine learning algorithms are not the same. In fact, there are three main types of machine learning: supervised, unsupervised and semi-supervised learning. The difference between these is in how they use data to make predictions or group things together (or both).
Supervised Learning
Supervised learning is used to predict a target value based on some input data. Imagine you want to know what someone’s age is just by looking at their picture–this would be an example of supervised learning because we already know what our target value (age) should be based on our input data (their picture). We could train an algorithm so that when given another person’s photo as input it would output their approximate age within some margin of error. This type of algorithm could then be used for any other person whose face we’ve never seen before!
Conclusion
Machine learning is a fascinating field of study, and it’s only going to get more important in the coming years. If you want to get involved with machine learning or just learn more about how it works, we recommend checking out our blog post on how to get started with artificial intelligence.
