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Implement customer segmentation, social sentiment, ecommerce recommendations, customer lifetime value and association rules within a broader 21-project Python ML portfolio. These outputs differ from the same creator’s retained…
Vinay Karode · Udemy
Implement customer segmentation, social sentiment, ecommerce recommendations, customer lifetime value and association rules within a broader 21-project Python ML portfolio. These outputs differ from the same creator’s retained churn/insurance/retail-sales course. Individual projects are not extra listings
Selected published requirements: “Basic understanding of Python programming language”. “Familiarity with fundamental mathematical concepts (statistics, probability, and algebra)”. See the provider page for the full requirements and current tool terms
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
Related tasks, tools and formats. Check the differences before choosing.
Vinay Karode
Implement logistic-regression and decision-tree churn models, interpret predictions and compare ensembles alongside broader insurance and retail-sales projects
John Wiley & Sons
The 24-module book-based course includes AI/ML behavioral segmentation and persona development, predictive lead scoring, customer lifetime value and dynamic pricing, churn models and retention measurement, NLP/sentiment, predictive…
Christ Raharja
Analyze campaign and retention metrics, use unsupervised customer segmentation, build CatBoost churn and MLP lifetime-value models, and run A/B tests within a broader Excel/Python/Power BI course