Four chapters (15 videos, 52 exercises) teach Apriori association rules and support, confidence, lift, conviction and related metrics, with interactive cross-selling/recommendation tasks for grocery, library, e-book, novelty retail and…
Four chapters (15 videos, 52 exercises) teach Apriori association rules and support, confidence, lift, conviction and related metrics, with interactive cross-selling/recommendation tasks for grocery, library, e-book, novelty retail and streaming examples
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Four chapters (16 videos, 53 exercises) implement marketing ML: logistic regression/decision trees and churn-driver interpretation on telecom data, RFM/linear-regression next-month CLV prediction for an online retailer, and k-means/NMF…
Intermediate Python, four hours. 16 videos and 53 interactive exercises. Prerequisite: Supervised Learning with scikit-learnCheck current priceAsk about teaching language
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
Kirill Eremenko , Hadelin de Ponteves , SuperDataScience Team , Ligency
Build ML models across domains, including association-rule market-basket analysis and reinforcement-learning CTR optimization, broad technical prerequisites and non-marketing models remain explicit
50 sections • 474 lectures • 49h 14m total lengthCheck current priceEnglish