08-03-2025, 03:14 PM
![[Image: c4b1d48d01729e390d1e244e63298c57.jpg]](https://i125.fastpic.org/big/2025/0803/57/c4b1d48d01729e390d1e244e63298c57.jpg)
Machine Learning with Python: k-Means Clustering
Duration: 49m | .MP4 1280x720, 30 fps® | AAC, 48000 Hz, 2ch | 127 MB
Genre: eLearning | Language: English
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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