Implement Machine Learning Using Oracle Data Miner
In this course, you’ll discover the basic concepts behind data science and machine learning, and learn about the steps associated with a typical machine learning project. You will learn about various techniques of predictive analytics and use Oracle Database 19c and Oracle SQL Developer with Data Miner 19.4 to create and deploy predictive analytics models
STUDENTS WILL LEARN TO
- The Fundamentals of Oracle Machine Learning
- Oracle Machine Learning UIs
- Classification Models
- Regression Models
- Clustering Models
- Anomaly Detection Models

Phone
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Phone
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Product
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Oracle
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Code
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D108288GC10
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Duration
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3 Days
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Price (baht)
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46,200
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About the course
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
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Schedule
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