Course 3B: Natural Language Processing in Driverless AI
By the end of this training, the attendee will be able to
- Create and tune a predictive model with one or multiple text columns
- Explain the differences between TF-IDF and word embedding approaches
- Locate and explain individual expert settings related to NLP models
- Using Projects, evaluate added value of text to numeric data when both are available
- Incorporate additional NLP models and transformers via recipes
- Evaluate MLI for NLP models
This course assumes
- Completion of “Introduction to Driverless AI” or equivalent experience.
- Completion of “Customizing Driverless AI with Recipes.”
- Familiarity with statistical or machine learning modeling.