Machine Learning with Python: k-Means Clustering (Updated: May 1, 2024)
Machine Learning with Python: k-Means Clustering
Updated: May 1, 2024
Duration: 50m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 127 MB
Genre: eLearning | Language: English
Updated: May 1, 2024
Duration: 50m | .MP4 1280x720, 30 fps(r) | 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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