Machine Learning: Random Forest with Python from Scratch - Clustering

The video tutorial introduces clustering, a branch of unsupervised learning in machine learning. It emphasizes the importance of understanding the difference between regression and classification before delving into clustering. Clustering involves grouping similar features without labels, unlike supervised learning. An example using lines and dots illustrates how clustering works. The tutorial also covers various clustering algorithms, including K-means, fuzzy C-means, EM, and hierarchical clustering, highlighting their unique implementations and applications. The next lecture will discuss the general flow of a machine learning project.

How do you determine which cluster a new data point belongs to?

What are some common algorithms used in clustering?