Linkedin Learning – Machine Learning With Python-K-Means Clustering UPDATED May 2024

Linkedin Learning – Machine Learning With Python-K-Means Clustering UPDATED May 2024
English | Tutorial | Size: 127.71 MB

Clustering-an unsupervised machine learning approach used to group data based on similarity-is used for work in network analysis, market segmentation, search results grouping, medical imaging, and anomaly detection. K-means clustering is one of the most popular and easy to use clustering algorithms. In this course, Fred Nwanganga gives you an introductory look at k-means clustering-how it works, what it’s good for, when you should use it, how to choose the right number of clusters, its strengths and weaknesses, and more. Fred provides hands-on guidance on how to collect, explore, and transform data in preparation for segmenting data using k-means clustering, and gives a step-by-step guide on how to build such a model in Python.

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