Watson Studio Methodology - eLearning

Course Outline

Data science and AI- Describe the value of artificial intelligence- Explain the AI ladder approach and AI lifecycle- Identify the roles for working with data and AI Watson Studio- Summarize the benefits of Watson Studio- Outline the integration of Watson Studio and Watson Machine Learning- List and explain the tools available in Watson Studio- Sign up for a free IBM Watson account Watson Machine Learning- Describe machine learning methods and how they fit with AI- Create a Watson Studio project for learning models Watson Knowledge Catalog- Explain the features of Watson Knowledge Catalog- Identify the role of data policies to govern data assets- List and describe the data files used in this course- Create a catalog, add assets to a catalog, and add catalog assets to a project Data refinement- List the steps to successful data mining- Describe the typical customer churn business problem- Identify the steps in the data refinement process- Shape a data set using the Data Refinery according to specific observations Data modeling- Differentiate the Watson Studio tools to create models- Create a Watson Machine Learning model using AutoAI- Create a Machine Learning model using SPSS Modeler- Build a model using SparkML Modeler Flow Data science with notebooks- Experiment with Jupyter notebooks- Load from a file and run a Jupyter notebook with Watson Studio Model deployment- Identify the model repository- List model deployment and test options- Deploy a model- Test a deployed model