What you would learn in Machine Learning with Python: k-Means Clustering course?
Unsupervised machine learning is a method employed to group data by similarity. It is utilized to perform network analysis as well as market segmentation as well as search results grouping medical imaging, and detection of anomalies. K-means clustering is among the most widely used and easy to apply clustering algorithms. In this class, Fred Nwanganga gives you an overview of K-means clustering: how it works and what it can be used for, the best time to employ it, and how to select the most appropriate number of clusters with the strengths as well as weaknesses and much more. Fred gives practical advice on how you can collect the data and investigate and transform data to segment data using K-means clustering. He also provides the steps to create the model using Python.
Course Content:
- Introduction
- 1. Understanding K-Means Clustering
- 2. Segmenting Data with K-Means Clustering
- Conclusion
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