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Oracle Machine Learning for R

About This Course

In this course, you will learn about Oracle Machine Learning for R, and learn how to use it to develop machine learning solutions.

3 Days

11 Lectures

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Course Content

  • 1 Overview of Oracle Machine Learning for R
  • Machine Learning
  • Objectives
  • Introduction to Oracle Machine Learning
  • Oracle Machine Learning Product Family
  • Oracle Machine Learning Notebooks
  • Oracle Machine Learning for SQL
  • Oracle Machine Learning for Python
  • Oracle Machine Learning Services
  • OML Services
  • Oracle Data Miner User Interface
  • Oracle Machine Learning: Key Attributes
  • Oracle Machine Learning Algorithms and Analytics
  • Why Oracle for Machine Learning?
  • R Implementation in Finance
  • What Is R?
  • R Environment
  • Oracle Machine Learning for R: Features
  • Oracle Machine Learning for R: Benefits
  • Oracle R Distribution
  • ROracle Package
  • Requirements for ROracle
  • Third-Party RStudio IDE
  • OML4R Architectural Components
  • OML4R: Efficiency All the Way!
  • Oracle Machine Learning for R: Installation Steps
  • Roadmap to Install OML4R
  • OML4R: Installation Environment
  • OML4R: Installing Oracle R Distribution
  • OML4R: Install Oracle Machine Learning for R Server
  • OML4R: Install Oracle R Distribution on Client
  • Oracle Machine Learning for R Packages in Client
  • How to Start OML4R in R Console from Client
  • Getting Help on OML4R Classes, Functions, and Methods
  • Getting Information on OML4R Classes, Functions, and Methods: Examples
  • Oracle Machine Learning for R: Global Options
  • Summary
  • 2 OML4R Transparency Layer: Introduction
  • Objectives
  • Transparency Layer: Overview
  • Invoking In-Database Aggregation Function
  • Overloads Graphics Functions for In-Database Statistics
  • Connecting to an Oracle Database Instance
  • Using ore.connect Function
  • Options for Connecting to Oracle Database
  • Using the ore.disconnect Function
  • Adding Schema with ore.attach
  • ore.frame Class
  • Data Types and Classes: Mapping R to Database Types
  • Working with Overloaded Functions in R
  • Support for Time Series Data Preparation
  • Time Series Data Preparation: Using Date and Time Arithmetic
  • Time Series Data Preparation: Comparing Dates and Times
  • In-Database Sampling
  • In-Database Sampling Techniques
  • In-Database Sampling: Simple Random Sampling
  • In-Database Sampling: Split Data Sampling
  • In-Database Sampling: Systematic Sampling
  • In-Database Sampling: Stratified Sampling
  • In-Database Sampling: Cluster Sampling
  • In-Database Sampling: Quota Sampling
  • Random Partitioning
  • Support for R Naming Conventions
  • Coercing R and Oracle Machine Learning for R Class Types
  • Ordering Framework: Creating Ordered and Unordered ora.frame Objects
  • Ordering Framework: ora.frame Ordered
  • Ordering Framework: ora.frame Unordered
  • Ordering Framework: Using Keys
  • Ordering Framework: Using Row Names
  • Ordering Framework: Using Ordered Frames
  • Global Options
  • Summary
  • 3 OML4R Transparency Layer: Create and Manage R Objects in Oracle Database
  • Objectives
  • R Object Persistence
  • R Object Persistence with OML4R
  • R Object Persistence: Advantages
  • OML4R Data Stores
  • ore.save() Function
  • ore.load() Function
  • ore.delete() Function
  • ore.datastore() Function
  • ore.datastoreSummary() Function
  • Create R Objects for In-Database Data
  • Synchronize Data with ore.sync() Function
  • Getting Objects with ore.get Function
  • Move Data to and from the Database
  • Ore.push Function
  • Ore.pull Function
  • Creating Database Tables
  • Deleting Database Tables
  • Ore.exists Function
  • Summary
  • 4 OML4R Transparency Layer: Data Preparation and Data Manipulation
  • Data Preparation
  • Learning Objectives
  • Preparing data in Database
  • Select Data
  • Selecting Data by Column
  • Select Data by Row
  • Select Data by Value
  • Using row indexing
  • Indexing an ore.frame Object
  • Combine Data
  • Aggregate Data
  • Transform Data
  • Exploratory Data Analysis Functions
  • dplyr
  • OREdplyr
  • Functionality of OREdplyr
  • OREdplyr functions
  • Examples: Selecting Columns
  • Examples: Programming with select_
  • Examples: Selecting Distinct Columns
  • Examples: Selecting Rows by Position
  • Examples: Arranging Columns
  • Examples: Filtering Columns
  • Examples: Mutating Columns
  • Examples: Joining Rows
  • Examples: Group Columns and Rows
  • Examples: Aggregate Columns and Rows
  • Examples: Sample Rows
  • Examples: Rank Rows
  • Select and Order Data
  • Using Third-Party Packages on the R Client
  • Installing and Loading a Third-Party Package on the R Client
  • Summary
  • 5 OML4R Embedded R Execution – R Interface
  • Objectives
  • Embedded R Execution
  • OML4R Embedded R Execution: R Interface Introduction
  • Benefits of Embedded R Execution
  • API for Embedded R Execution
  • User-Defined R Functions for Embedded R Execution
  • Installing Package for a Single Database
  • Running User-Defined R Functions Using R Interface
  • Functionality of Automatic Connection
  • Automatic Connection in Embedded R Scripts
  • Example to Check Connection
  • Using the ore.doEval Function
  • Using the tableApply Function
  • Using the groupApply Function
  • Using the rowApply Function
  • Using the indexApply Function
  • OML4R-Defined Graphics Function
  • Viewing database server-generated graphics in client
  • Using ore.indexApply for simulation
  • OML4R-Defined Graphics Function Examples
  • Summary
  • 6 OML4R Embedded R Execution – SQL Interface
  • Objectives
  • SQL Interface for Embedded R Execution
  • Create a User-Defined R Function Using the SQL Interface
  • SQL API for Oracle Machine Learning for R
  • rqEval Function
  • Embedded R Execution: SQL Interface
  • rqTableEval: Singleton/Real-Time Scoring
  • “rqGroupEval” Functionality
  • “rqGroupEval” Build and rqRowEval Score
  • Parameters of the SQL Table Functions
  • Returning R Statistical Results as a Database Table
  • Wrap in Function, Invoke from ore.tableApply
  • Determine What Results We Really Need
  • Create Script in Repository Using SQL
  • Invoke from SQL Select Statement
  • Manage User-Defined R Functions Using the SQL Interface
  • Using Access Privileges with User-Defined Functions
  • Manage User-Defined R Functions and Datastores in SQL
  • PL/SQL Procedures for Managing R Scripts and Datastores
  • Data Dictionary Views for Datastores
  • Manage Datastores in SQL – Example
  • Return Value of Embedded Execution SQL Table Functions Using Pre-Defined
  • Graphics Function
  • Summary
  • 7 Modeling in OML4R: Part 1
  • Objectives
  • Supported ML Techniques
  • ORE Package Overview
  • OREdm Package
  • OREdm Algorithm Input Requirements
  • OREdm Features
  • OREdm Algorithms
  • Association Rules – Apriori Algorithm
  • Using the ore.odmAssocRules Function
  • Attribute Importance – Minimum Description Length
  • Classification – Decision Tree
  • Clustering – Expectation Maximization
  • Using the ore.odmEM Function: Example
  • Using the ore.odmEM Function: Output
  • Feature Extraction – Explicit Semantic Analysis (ESA)
  • Using the ore.odmESA Function
  • Generalized Linear Models
  • Building a Linear Regression Model
  • Using Ridge Estimation for Coefficients of ore.odmGLM Model
  • Building a Logistic Regression GLM
  • Specifying a Reference Value in Building a Logistic Regression GLM
  • Clustering – K-Means
  • Clustering – K-Means Features
  • Using the ore.odmKM Function: Example
  • Using the ore.odmKM Function: Output
  • Classification – Naïve Bayes
  • Using the ore.odmNB Function
  • Feature Extraction Non-negative Matrix Factorization (NMF)
  • Using the ore.odmNMF Function
  • Clustering – Orthogonal Partitioning
  • Limitation of Orthogonal Partitioning
  • Using the ore.odmOC Function
  • Feature Extraction – Singular Value Decomposition
  • Using the ore.odmSVD Function
  • Support Vector Machine
  • Using the ore.odmSVM Function
  • Partitioned Models
  • Summary
  • 8
  • Modeling in OML4R: Part 2
  • Objectives
  • OREmodels Package
  • Linear Model
  • Linear Model Example
  • Stepwise Linear Regression
  • Stepwise Regression Example
  • Generalized Linear Models
  • Using the ore.glm Function
  • Neural Networks
  • Using ore.neural and Specifying Activations
  • Random Forest
  • Using ore.randomForest
  • Singular Value Decomposition
  • SVD Example Using ore.frame
  • Visualization Function: Example
  • Principal Component Analysis
  • Using the prcomp and princomp Functions
  • Summary
  • 9 Working with ROracle
  • Objectives
  • Overview of ROracle
  • Reading Database Table to R data.frame
  • Writing Database Table from R data.frame
  • ROracle 1.3-2 Enhancements
  • Connect to an extproc for Use within OML4R Embedded R Execution
  • Unload Driver
  • Get Info Methods
  • Read/Write Table Methods
  • Using Attributes to Map NCHAR, CLOB, BLOB, NCLOB Columns Correctly with
  • dbWriteTable
  • Insert date and time data into a table
  • Insert and update data in a table
  • Send Query Methods
  • Invoking Stored Procedures
  • Summary
  • 10 OML4R Statistics Engine
  • Objectives
  • Statistics Engine Overview
  • OML4R Statistics Engine
  • Significance Tests
  • Distribution Functions
  • Additional Functions
  • Base SAS Equivalent Functionality
  • OML4R Statistical Functions
  • ore.summary
  • Statistics Supported by ore.summary
  • ore.summary Parameters
  • ore.summary Examples
  • ore.rank
  • ore.rank Parameters
  • ore.rank Examples
  • ore.sort
  • ore.sort Parameters
  • ore.sort Examples
  • Example Output
  • ore.corr
  • ore.corr Parameters
  • ore.corr Examples
  • ore.crosstab
  • ore.crosstab Parameters
  • ore.corr Examples
  • ore.crosstab Examples
  • ore.freq
  • ore.freq Parameters
  • ore.freq Examples
  • ore.esm
  • ore.esm Example
  • ore.esm Output
  • ore.univariate
  • ore.univariate Parameters
  • ore.univariate Examples
  • Summary
  • 11 OML4R Best Practices
  • Objectives
  • Managing Memory Limits
  • Computing and Setting Memory Limits for Embedded R
  • Open Source Packages
  • Should I use a third-party package on database data?
  • Machine Learning Interface: Benefits
  • Transparency Layer and Machine Learning: Memory Management
  • Considerations
  • Explicitly Specifying Oracle Database Parallelism
  • Explicitly Specifying OML4R Parallelism
  • Embedded R Execution Initial Memory Management Considerations
  • Memory Consumed in R
  • Datastore: Benefits
  • Datastore: R Object Persistence
  • Object Migration
  • Summary

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