IBM GTP Award 2018

Watson Studio Methodology - eLearning

Course Details
Code: W7067G-WBT
Tuition (USD): $99.00 • Self Paced (6 hours)
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This IBM Web-Based Training (WBT) is Self-Paced and includes:
- Instructional content available online for duration of course
- Visuals without hands-on lab exercises

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.

Skills Gained

  • Data science and AI
  • Watson Studio
  • Watson Machine Learning
  • Watson Knowledge Catalog
  • Data refinement
  • Data modeling
  • Data science with notebooks
  • Model deployment

Who Can Benefit

Data scientists, data engineer, business analyst



Course Details

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 

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