Build reliable supervised classifiers from marketing text and defensible human labels. Learners create coding rules, reconcile coders into gold-standard labels, transform text into predictive features, train a regularized elastic-net model, separate training from validation evidence, and use errors and learning curves to judge model quality and the value of collecting more labeled data.

Supervised Text Classification for Marketing Analytics

Supervised Text Classification for Marketing Analytics
This course is part of Text Marketing Analytics Specialization


Instructors: Chris J. Vargo
Access provided by Charotar University of Science and Technology
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What you'll learn
Distinguish supervised classification from unsupervised text-learning tasks, create a labeling codebook, and reconcile multiple coders into gold-standard labels.
Transform raw text into a model-ready predictive feature matrix and configure and train a regularized elastic-net text classifier.
Evaluate generalization with held-out validation data and appropriate classification metrics.
Interpret learning curves to identify overfitting and diminishing returns from additional labels.
Skills you'll gain
- Applied Machine Learning
- Deep Learning
- Feature Engineering
- Text Mining
- Embeddings
- Machine Learning
- Statistical Machine Learning
- Model Evaluation
- Model Training
- Data Preprocessing
- Predictive Modeling
- Transfer Learning
- Machine Learning Algorithms
- Data Manipulation
- Data-Driven Marketing
- Supervised Learning
- Marketing Analytics
Tools you'll learn
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There are 4 modules in this course
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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