Machine Learning Rapid Prototyping with IBM Watson Studio

Building a rapid prototype of Watson Studio AI - Describe the benefits of AutoAI for rapid prototyping - Identify implementations of AutoAI - Become familiar with the Watson Studio platform - Build rapid prototypes using Watson Studio AutoAI - Generate a Python notebook of the prototype with one click Automated Data Preparation and Model Selection - Evaluate the data preprocessing steps for the use cases - Refine data preprocessing using the AutoAI-generated Python notebook - Examine the model selection outcome for use cases - Refine the Python notebook to make changes to the selected model Automated Feature Engineering and Hyperparameter Optimization - Explain how the Cognito algorithm can save time by automating feature engineering - Evaluate the automated feature engineering performance for the use cases - Describe several strategies for HPO in order of increasing sophistication - Observe how changes to the model hyperparameters in the Python notebook affect the prototype's performance Evaluation and Deployment of AutoAI-generated Solutions - Evaluate the prototype for further development or deployment based on calculated performance metrics - Deploy the prototype using Watson Machine Learning  

This course is intended for practicing Data Scientists. While it showcases the automated AI capabilities 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)

• Building a rapid prototype of Watson Studio AI
• Automated Data Preparation and Model Selection
• Automated Feature Engineering and Hyperparameter Optimization
• Evaluation and Deployment of AutoAI-generated Solutions