Description

This course is ideal for those that are interested in data mining, and it is a beginner course. You should have a beginner to intermediate understanding of Python as I don’t spend a lot of time on the programming aspect.Most data in the world (whether text,audio,visual, etc) is raw or unlabeled. This is precisely the reason that unsupervised machine learning has become so important. By using certain approaches to unsupervised machine learning (like clustering) we can discover patterns or underlying structures in data. This is a major component of exploratory data mining. Furthermore, when one does exploratory data mining, it is used to draw hypotheses, assess assumptions about our statistical inferences, and its used as a basis for further research. For example, the conclusion of a cluster analysis could result in the initiation of a full scale experiment.
The course covers two of the most important and common non-hierarchical clustering algorithms, K-means and DBSCAN using Python.

If you are interested in data mining, and want to get a taste of how it works, this course is a great introduction!
Who is the target audience?

    Students interested in clustering techniques and unsupervised machine learning
    Interest in data mining and/or data analysis

Requirements

    Understanding Python at beginner or intermediate level is mandatory