Data science, machine learning, and analytics without coding
Build a KNIME Random Forest model for a sales-and-marketing client, prepare data and interpret predictions within a broader no-code data-science course
Build a KNIME Random Forest model for a sales-and-marketing client, prepare data and interpret predictions within a broader no-code data-science course
Audience and starting point
Selected published requirements: “A computer with enough space to install the KNIME Analytics Platform”. See the provider page for the full requirements and current tool terms
Access and subscriptions
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
An original example for comparing learning plans. Ask whether the course teaches this task, includes practice and offers feedback; the diagram does not describe a provider’s course.
Use a no-code classification model and forward feature selection to analyze age, geography and traffic-source predictors and derive marketing actions within a broader RapidMiner ML course
14 sections • 63 lectures • 2h 37m total lengthCheck current priceEnglish
Shared topic: Customer analytics and personalization
Spaark Hub, Gaurav Shandilya
Prepare campaign data, model Google Ads outcomes using Random Forest and Gradient Boosting, compare channels and benchmarks, and apply cohort/RFM analysis to retention and ROI
12 sections • 111 lectures • 14h 18m total lengthCheck current priceEnglish
Shared topic: Customer analytics and personalization
Shreesha Jagadeesh
Build an iterative customer-churn classification project using baseline Random Forest, feature engineering and tuned ensembles within a broader fraud/churn/financial-risk compilation
6 sections • 58 lectures • 3h 49m total lengthCheck current priceEnglish