Clustering in Machine Learning
In this course, you’ll define clustering for ML applications, prepare data for clustering, define similarity for your dataset, compare manual and supervised similarity measures, use the k-means algorithm to cluster data, evaluate the quality of your clustering result.
Interest in learning Machine Learning? This free course on Clustering from Google is for you.
Clustering in Machine Learning course description
When you’re trying to learn about something, say music, one approach might be to look for meaningful groups or collections. You might organize music by genre, while your friend might organize music by decade. How you choose to group items helps you to understand more about them as individual pieces of music. You might find that you have a deep affinity for punk rock and further break down the genre into different approaches or music from different locations.
On the other hand, your friend might look at music from the 1980’s and be able to understand how the music across genres at that time was influenced by the sociopolitical climate. In both cases, you and your friend have learned something interesting about music, even though you took different approaches.
In machine learning too, we often group examples as a first step to understand a subject (data set) in a machine learning system. Grouping unlabeled examples is called clustering.
- Define clustering for ML applications.
- Prepare data for clustering.
- Define similarity for your dataset.
- Compare manual and supervised similarity measures.
- Use the k-means algorithm to cluster data.
- Evaluate the quality of your clustering result.