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. This course is intended for practicing Data Scientists. While it showcases the automated AI capabilies of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts. In order to be successful, you should have knowledge of: Data Science workflow Data Preprocessing Feature Engineering Machine Learning Algorithms Hyperparameter Optimization Evaluation measures for models Python and scikit-learn library (including Pipeline class)