Unsupervised Learning in Python

Imagine that you have customers with different characteristics. For example, their location, age and financial history. You need to identify patterns and group them together. A collection of text, such as Wikipedia pages, might be available that you wish to organize into different categories according their content. Unsupervised learning refers to unsupervised learning. Unsupervised learning is the absence of supervision or guidance in pattern discovery via a prediction task. Instead, you discover hidden structures using unlabeled data. Unsupervised learning can be used to many machine learning techniques including clustering, matrix factorization, dimension reduction, and matrix factorization. This course will cover the basics of unsupervised learning and how to implement the most critical algorithms using scikit-learn/scipy. This course will show you how to analyse, cluster, transform and visualize unlabeled data, as well as extract insights. This course also contains a recommendation system, which can be used for recommending musical artists.