K-Means Clustering in Python Video
- Understand K-Means Clustering and it’s advantages and disadvantages.
- Choose the best value for K where K is the number of clusters, using the Elbow, Silhouette, and Gap Statistic methods.
- Create a K-means clustering model in Python.
- Practice the steps of initializing, assigning, and updating to implement K-means clustering in Python using the jupyter notebook.
- Perform mini batch clustering in Python.
- Learn why mini-batch is important in K-Means clustering and how it works on data sets.
- Perform the K-Means Clustering Evaluation Method. Practice applying four evaluation methods: Sum of Squared Error Method, Scatter Criteria, Rand Index, and the Precision Recall Measure.
- Predict values based upon the K-Means Clustering model.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date