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.
Details
Course Code: W7067G
Brand: Cloud & Data Platform
Category: Cloud
Skill Level: Basic
Duration: 6.00H
Modality: WBT
Audience
Data scientists, data engineer, business analyst
Prerequisites
None
Topic
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
Objectives
- Data science and AI
- Watson Studio
- Watson Machine Learning
- Watson Knowledge Catalog
- Data refinement
- Data modeling
- Data science with notebooks
- Model deployment