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Watson Studio Methodology - eLearning

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...

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$249 USD
Course Code W7067G-WBT
Duration 6 hours
Available Formats Self Paced
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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

Prerequisites

None

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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