Prepare customer data, implement k-means in Python, evaluate and interpret clusters and complete a customer-segmentation case study
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Google Colab is the sole tool listed in the requirements. The capture does not state a paid tier or an included software license
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Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
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Prepare and evaluate k-means, hierarchical and DBSCAN clusters, then complete an ecommerce customer-segmentation project, broader unsupervised ML supports the marketing application
11 sections • 52 lectures • 4h 53m total lengthCheck current priceEnglish
Prepare customer data in Python, apply supervised regression and unsupervised k-means/PCA, evaluate clusters and translate segments into targeted marketing actions
7 sections • 45 lectures • 3h 21m total lengthCheck current priceEnglish
Four chapters (17 videos, 55 exercises) use online-retailer transactions for cohort acquisition/retention metrics, RFM value scoring and custom segments, then prepare and scale RFM features, fit k-means and interpret the resulting…
Intermediate Python, four hours. 17 videos and 55 interactive exercises. Prerequisite: Supervised Learning with scikit-learnCheck current priceAsk about teaching language