Unsupervised learning is a type of machine learning in which the algorithm is given a data set and is asked to find patterns and relationships within the data without being given any labels or other information about the data. In other words, the algorithm is not told what to look for, but is instead left to discover the underlying structure of the data on its own. This type of learning is called unsupervised because the algorithm is not given any guidance or supervision on what it should be looking for. Examples of unsupervised learning algorithms include clustering and dimensionality reduction.
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more
The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? What song to listen to next? Nowadays, the internet and its vast content offer too many choices. But there is hope – recommender systems are here to solve this problem and support our decision-making. They … Read more
