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W7067G - Watson Studio Methodology

Overview

Duration: 1 day

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.

Objectives

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

Content

1. 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
  • 2. Watson Studio
  • Summarize the benefits of Watson Studio
  • Outline the integration of Watson Studio and Watson Machine
  • 3. Learning

  • List and explain the tools available in Watson Studio
  • Sign up for a free IBM Watson account
  • 4. Watson Machine Learning
  • Describe machine learning methods and how they fit with AI
  • Create a Watson Studio project for learning models
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. Data science with notebooks
  • Experiment with Jupyter notebooks
  • Load from a file and run a Jupyter notebook with Watson Studio
  • 9. Model deployment
  • Identify the model repository
  • List model deployment and test options
  • Deploy a model
  • Test a deployed model
  • Audience

    Data scientists, data engineer, business analyst

    Prerequisites

    None

    Certification

    Schedule

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