Overview

Quickly build and train machine learning models with Watson AutoAI aims to familiarize data science and analytics professionals with the fundamentals of theWatson Studio’s AutoAI tool. The course walks users through the process of creatingIBM Cloud projects, and building and evaluating AutoAI experiments for various supervised machine learning use cases. 

The lab environment for this course uses IBM Cloud.

Audience

This course is designed for data analysts, data scientists, and machine learning specialists.

Prerequisites

• IBM Cloud access
• Knowledge of supervised machine learning use cases
• Knowledge of Python code in notebook environments
• Basic knowledge of machine learning evaluation metrics

Objective

• Set up an IBM Cloud Account and project and add and manage associated resources
• Identify potential machine learning use cases applicable to AutoAI
• Differentiate problem types relevant for AutoAI experiments (Classification, Regression, Time Series)
• Configure settings for various AutoAI experiments
• Evaluate pipelines and models that are produced by AutoAI experiments
• Recognize deployment strategies for AutoAI models

Course Outline

• Course introduction
• Introducing IBM Cloud
• What is AutoAI?
• Machine learning with AutoAI: Classification
• Machine learning with AutoAI: Regression
• AutoAI deployments at a glance
• Machine learning with AutoAI: Time series
• Fairness evaluation in AutoAI machine learning models