Implement Machine Learning Using Oracle Data Miner

COURSE OUTLINE

MODULE 01 Course Overview

• Course Overview

MODULE 02 Fundamentals of Oracle Machine Learning

• Fundamentals of Oracle Machine Learning Part 1
• Fundamentals of Oracle Machine Learning Part 2

MODULE 03 Introduction to Oracle Machine Learning Uls

• Introduction to Oracle Machine Learning Uls Part 1
• Introduction to Oracle Machine Learning Uls Part 2
• Practice 3-1: Create a SQL Developer Connection for the Data Miner User
• Practice 3-2: Install the Data Miner Repository
• Practice 3-3: Create a Data Miner Workflow

MODULE 04 Using Classification Models

• Using Classification Models Part 1
• Using Classification Models Part 2
• Practice 4-1: Select and Examine Titanic Data Source
• Practice 4-2: Perform Transformations to Prepare the Data
• Practice 4-3: Use Attribute Importance to Filter Input Variables
• Practice 4-4: Create Classification Models
• Practice 4-5: Create Classification Models Using Oracle Data Miner Automated OMLw

MODULE 05 Using Regression Models

• Using Regression Models Part 1 Using Regression Models Part 2
• Practice 5-1: Select and Examine Boston Housing Data Source
• Practice 5-2: Perform Transformations to Prepare the Data
• Practice 5-3: Use Attribute Importance to Filter Input Variables
• Practice 5-4: Create Regression Models
• Practice 5-5: Create Regression Models Using Oracle Data Miner Automated OML

MODULE 06 Using Clustering Models

• Using Clustering Models Part 1 Using Clustering Models Part 2
• Practice 6-1: Select and Examine Life Insurance Customers? Data Source
• Practice 6-2: Create Clustering Models
• Practice 6-3: Select and Examine the IRIS Flower Dataset
• Practice 6-4: Create Clustering Models
• Practice 6-5: Create K-Means Clustering Model Without the Species Attribute
• Practice 6-6: Compare the KMeans Models with and Without the SPECIES Column

MODULE 07 Using Anomaly Detection Models

• Using Anomaly Detection Models Part 1
• Using Anomaly Detection Models Part 2
• Practice 7-1: Select and Examine the Auto Insurance Claims Dataset
• Practice 7-2: Create Anomaly Detection Model.
• Practice 7-3: Create Anomaly Detection Model for the Tax Dataset