![Machine Learning with Python: k-Means Clustering [Repost]](https://pixhost.icu/avaxhome/bf/c1/00acc1bf_medium.jpg)
Machine Learning with Python: k-Means Clustering [Repost]
![Machine Learning with Python: k-Means Clustering [Repost]](https://pixhost.icu/avaxhome/bf/c1/00acc1bf.jpg)
Machine Learning with Python: k-Means Clustering
.MP4, AVC, 1280x800, 30 fps | English, AAC, 2 Ch | 49m | 127 MB
Instructor: Frederick Nwanganga
.MP4, AVC, 1280x800, 30 fps | English, AAC, 2 Ch | 49m | 127 MB
Instructor: Frederick Nwanganga
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.
![Machine Learning with Python: k-Means Clustering [Repost]](https://pixhost.icu/avaxhome/c0/c1/00acc1c0_medium.jpg)
Machine Learning with Python: k-Means Clustering [Repost]
![Machine Learning with Python: k-Means Clustering [Repost]](https://pixhost.icu/avaxhome/c0/c1/00acc1c0.jpg)