Data science, machine learning, and analytics without coding
Eric Hulbert
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
Related skills · check the marketing fit
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
Ram Prasad · Udemy
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
Selected published requirements: “Able to use a Windows computer and install software on it”. See the provider page for the full requirements and current tool terms
Practice: The outline lists knowledge checks on classification and clustering
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.
Eric Hulbert
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
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
DataCamp
Builds click-through-rate prediction from advertising data: feature creation, classification/decision trees, cross-validation, regularization, random forests and grid-search tuning, evaluates predictions against ad-spend ROI