Unsupervised Learning and Its Applications in Marketing
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
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
Access and subscriptions
The official provider listing offers an online course entry point. Sign-in, enrollment, checkout and learner access were not tested
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.
Prepare and evaluate k-means, hierarchical and DBSCAN clusters, then complete an ecommerce customer-segmentation project, broader unsupervised ML supports the marketing application
11 sections • 52 lectures • 4h 53m total lengthCheck current priceEnglish
Prepare customer data in Python, apply supervised regression and unsupervised k-means/PCA, evaluate clusters and translate segments into targeted marketing actions
7 sections • 45 lectures • 3h 21m total lengthCheck current priceEnglish
Prepare customer data, apply hierarchical/k-means/PCA segmentation, analyze purchase data by segment and model purchase incidence, marketing mix and price response
13 sections • 76 lectures • 5h 11m total lengthCheck current priceEnglish