Released 05/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 49m | Size: 127.7 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.
Homepage
Machine Learning with Python: k-Means Clustering Online Class | LinkedIn Learning, formerly Lynda.com
Learn the basics of k-means clustering, one of the most popular unsupervised machine learning approaches.
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