K-means is an algorithm that can separate unlabeled data into a predetermined number of clusters. In this blog post, we look at its underlying principles, use cases, as well as benefits and limitations.
The k-nearest neighbors (kNN) algorithm is a simple tool that can be used for a large number of real-world problems. In this article, we cover what kNN is, how it works, and how to implement it in machine learning projects.
In this article, you will read about different types of outliers and machine learning techniques that help to find anomalies. Learn how you can apply ML anomaly detection techniques to fraud prevention, medical diagnosis, and more.
Self-supervised learning is one of the most popular approaches to ML today. SSL algorithms don’t require manual target labeling and can obtain all the information they need from the data. Find out more about how they work in our new post.
Naive Bayes classifiers are a set of classification algorithms for binary (two-class) and multiclass problem classification. Let’s find out where the Naive Bayes algorithm has proven to be effective and where it hasn't.
The difference between deep learning and neural networks is often confusing for beginners. What does it mean for an algorithm to be “deep”? What types of neural networks exist out there? You’ll learn all that and more in our guide.