Watson Studio Methodology – eLearning – W7067G WBT

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.



Course Code: W7067G

Brand: Cloud & Data Platform

Category: Cloud

Skill Level: Basic

Duration: 6.00H

Modality: WBT



Data scientists, data engineer, business analyst






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

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