In this 6 hour web-based training (WBT), you will learn how Watson Discovery lets business analysts and developers rapidly build cognitive, cloud-based exploration applications that unlock actionable insights hidden in unstructured data — including your own proprietary data, as well as public and third-party data.

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.

In this course, you will explore data preparation, data modeling, data visualization, and data cataloging using Watson Studio, Watson Knowledge Catalog, and Watson Machine Learning.