Machine Learning Rapid Prototyping with IBM Watson Studio
Discover the power of automated AI with IBM Watson Studio’s AutoAI. This course is designed for practicing Data Scientists, providing an end-to-end automated pipeline for rapid-prototyping models. Enhance your knowledge of Data Science workflow, feature engineering, and hyperparameter optimization. Gain insights into the underlying technology developed by IBM Research. Get hands-on experience with auto-generated Python notebooks and test data sets. Prior knowledge of Python, scikit-learn library, and evaluation measures for models is recommended. Take your AI capabilities to the next level with this comprehensive course.
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)
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