Compare AI training for customer analytics, marketing data and interpreting campaign results.
333 courses in this collection
University of Colorado System
Marketing applications of R-based CART and random forests, cross-validation/model comparison, causal trees and forests for targeting, segmentation, principal-component analysis and recommender systems
O.P. Jindal Global University
Python/scikit-learn supervised workflows include marketing-engagement prediction, product recommendation, three-month customer lifetime value, neural-network churn prediction and model deployment
O.P. Jindal Global University
Python marketing workflows cover k-means, hierarchical and DBSCAN segmentation, PCA, t-SNE and autoencoders, anomaly detection, association mining, semi-supervised methods, and recommender systems
O.P. Jindal Global University
Marketing text-mining curriculum applies sentiment, topic modeling, NLP, named-entity recognition, classification, topic clustering and predictive analysis to customer reviews, social posts, feedback and news. Named uses include…
University of Colorado Boulder
Python/JSON practicals apply unsupervised text analysis to YikYak sentiment segmentation and Amazon reviews, with TF-IDF/topic models, BERTopic and text networks
University of Colorado Boulder
Practical workflow defines a codebook and gold labels, constructs a feature matrix, trains an elastic-net text classifier, then validates on held-out data using metrics and learning curves, Python/scikit-learn/TensorFlow are listed
DataCamp
R workflows use linear regression for customer lifetime value, logistic regression for churn, Kaplan-Meier/Cox survival analysis for time to reorder and churn, and PCA on CRM data
Coursera
Google Analytics audience segmentation and export, AI-assisted sentiment/theme analysis and NLP clustering, A/B validation, churn model checks using precision/recall, and automated Looker Studio dashboards/reports
DataCamp
Dedicated Telco Churn workflow: exploratory analysis, feature selection/engineering, supervised scikit-learn prediction, train/test split, confusion matrix and accuracy/precision/recall/ROC-AUC/F1, tuning and feature importance
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
University of Illinois Urbana-Champaign
Marketing analytics applies causal analysis and AI/ML predictive models to customer behavior, text/sentiment/topic/network analyses to user, firm and AI-generated content, and conjoint analysis to preference, examples address CLV, churn…
O.P. Jindal Global University
Marketer-facing decision workflows use CLV/value and lifecycle, likelihood to buy/engage, personalized recommendations, clustering/personas, remarketing/lookalikes, churn and marketing automation/data governance. This public outline is…
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