Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated numeric variables into a set of values ...

Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning.

Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns.

Dimensionality reduction – assuming the data can be compressed while preserving data integrity. Everyday algorithms we use are lossy compression formats such as JPEG and MP3. We also use principal ...

Here are the differences between supervised, semi-supervised, and unsupervised learning -- and how each is valuable in the enterprise.

Machine learning defined Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.

Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms features into a new set of uncorrelated variables called principal components.

First, Independent Component Analysis is used to determine a set of derived independent components that are linear combinations of 47 observed features (namely, ionized lines, Lick indices, ...