Machine Learning with Python: k-Means Clustering
With Frederick Nwanganga
Liked by 547 users
Duration: 50m
Skill level: Intermediate
Released: 5/23/2022
Course details
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.
Skills you’ll gain
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Meet the instructor
Learner reviews
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Gideon Olayinka
Gideon Olayinka
MSc Big Data Analytics Student | Data Analyst | Data Cleaning, Analysis & Visualization | SQL • Python • Power BI • Excel • R
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Navya Rayi
Navya Rayi
IT’27 @ Aditya Engineering College || Full Stack Developer || Java Certified (Oracle Academy) || DSA Practitioner || Creative Web Designer || @…
Contents
What’s included
- Practice while you learn 1 exercise file
- Test your knowledge 1 quiz
- Learn on the go Access on tablet and phone
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