Oracle Machine Learning with R

Oracle Machine Learning with R

Duration : 3 Days (24 Hours)

Oracle Machine Learning with R Course Overview:

The Oracle Machine Learning with R certification is a professional qualification that validates an individual’s proficiency in using Oracle’s R Enterprise software. It demonstrates the ability to employ this tool for data analysis, manipulation, visualization, and predictive modeling.

By leveraging the R language in conjunction with Oracle’s database capabilities, this certification enables professionals to perform advanced analytics. Industries utilize this skillset to manage, retrieve, and analyze large data sets effectively, often employing algorithms to make informed decisions. This approach enhances various aspects of business operations, including business intelligence, risk management, customer profiling, and product development.

With this certification, individuals can effectively utilize R in Oracle’s environment, leading to improved business productivity and outcomes. It forms a crucial component of industries’ efforts to harness data-driven insights and drive better decision-making processes.

Intended Audience:

  • Developers interested in machine learning
  • Data analysts and data scientists
  • Oracle database administrators
  • R programmers looking to integrate machine learning into applications
  • IT professionals working on AI-based projects
  • Students studying data science or AI
  • Professionals seeking career growth in data analysis and machine learning

Learning Objectives of Oracle Machine Learning with R:

The learning objectives of the Oracle Machine Learning with R course are as follows:

  1. Equipping students with essential skills for comprehensive data analytics.
  2. Learning to use Oracle’s SQL interface to manage data efficiently.
  3. Acquiring proficiency in R, a popular language for statistical analysis.
  4. Creating, evaluating, and deploying machine learning models using Oracle’s ML algorithms.
  5. Understanding advanced analytics and data visualization techniques.
  6. Using R in conjunction with Oracle databases to analyze and interpret complex data effectively.

Module 01:

  • Overview of Oracle Machine Learning for R
  • Oracle Machine Learning Notebooks
  • Oracle Machine Learning: Key Attributes
  • Oracle R Distribution
  • Oracle Machine Learning for R: Installation Steps
  • Practice I-I: Understanding the Lab Environment
  • Practice 1-2: Connect to the R console
  • Practice 1-3: Connect to RStudio Server
  • Practice 1-4: Run the Sample Scripts

Module 02:

  • OML4R Transparency Layer: Introduction
  • Using ore.connect Function
  • In-Database Sampling
  • Ordering Framework Creating Ordered and Unordered ore.frame Objects
  • Practice 2-1: Using the ore.connect Function
  • Practice 2-2: Working with OML4R Transparency Layer
  • Practice 2-3: Additional Transparency Layer Functions
  • Practice 2-4: Working with ore.Frame Database Table Proxy Object
  • Practice 2-5: Using Scale() and Transform() Functions
  • Practice 2-6: Sampling Data
  • Practice 2-7: Using the core.disconnect Function

Module 03: OML4R Transparency Layer: Create and Manage R Objects in Oracle Database

  • Introduction to OML4R Transparency Layer
  • Function
  • Create R Objects for In-Database Data
  • Move Data to and from the Database
  • Practice 3-1: Get Objects with ore.get Function
  • Practice 3-2: Using ore.Create, ore.Push, and ore.Drop
  • Practice 3-3: Create and Manage R Object in Oracle Database using R Data Store

Module 04: OML4R Transparency Layer: Data Preparation and Data Manipulation

  • Introduction to OML4R Transparency Layer
  • Using row indexing
  • Exploratory Data Analysis Functions
  • Using Third-Party Packages on the R Client
  • Practice 4-1: Basic data manipulation using OREdplyr
  • Practice 4-2: Stacking and Grouping using OREdplyr
  • Practice 4-3: Chaining Using OREdplyr
  • Practice 4-4: Implementing Rank Function using OREdplyr
  • Practice 4-5: Aggregate the Column values using the OREdplyr
  • Practice 4-6: Joining data using OREdplyr

Module 05: OML4R Embedded R Execution — R Interface

  • Introduction to OML4R Embedded R Execution — R Interface
  • User-Defined R Functions for Embedded R Execution
  • Functionality of Automatic Connection
  • Using the ore.doEval Function
  • OML4R-Defined Graphics Function
  • Practice 5-1: Using ore.doEval() with R Script Repository
  • Practice 5-2: Using ore.table.Apply()
  • Practice 5-3: Using Embedded R Execution Functions: ore.groupApply, ore.indexApply
  • Practice 5-4: Best Practice Workflow for Developing Deployable User-Defined Functions

Module 06: OML4R Embedded R Execution — SQL Interface

  • OML4R Embedded R Execution — SQL Interface
  • SQL API for Oracle Machine Learning for R
  • Returning R Statistical Results as a Database Table
  • Manage User-Defined R Functions using the SQL Interface
  • PL/SQL Procedures for Managing R Scripts and Datastores
  • Practice 6-1: Build an 1m Model by using rqTable Eval()
  • Practice 6-2: Score Data in Batch Mode Using rqTable Eval()
  • Practice 6-3: Passing Arguments to User-Defined Functions using SQL API
  • Practice 6-4: Using the SQL Datastore API
  • Practice 6-5: Additional Code Samples

Module 07: Modeling in OML4R: Part 1

  • Modeling in OML4R: Part I
  • OREdm Package
  • Feature Extraction – Explicit Semantic Analysis (ESA)
  • Generalized Linear Models
  • Clustering – K-Means
  • Classification – Naive Bayes
  • Feature Extraction Non-negative Matrix Factorization (NMF)
  • Partitioned Models
  • Practice 7-1: using Ore.odmGLM
  • Practice 7-2: Using Ore_odmSVM
  • Practice 7-3: using Ore_odmKMeans
  • Practice 7-4: using Ore.odmAssocRules
  • Practice 7-5: Using ERE Framework algorithms
  • Practice 7-6: Identifying Frequently-Purchased Groceries

Module 08: Modeling in OML4R: Part 2

  • Introduction
  • Neural Networks
  • Singular Value Decomposition
  • Practice 8-1: using ore.glm Function
  • Practice 8-2: Using ore.neural Function
  • Practice 8-3: using ore.randomForest Function
  • Practice 8-4: Usecase-Estimating Wine Quality

Module 09: Working with ROracle

  • Introduction
  • Connect to an extproc for Use within OML4R Embedded R Execution
  • Read/Write Table Methods
  • Practice 9-1: using the ROracle Interface

Module 10: OML4R Statistics Engine

  • OML4R Statistics Engine
  • ore_summary
  • ore.rank
  • ore.sort
  • ore.corr
  • ore.crosstab
  • ore.freq
  • ore.esm
  • ore.univariate
  • Practice 10-1: Working with OML4R Statistical Functions
  • Practice 10-2: Using ore.summary Function
  • Practice 10-3: Using ore.rank Function
  • Practice 10-4: using ore.sort Function

Module 11: OML4R Best Practices

  • Introduction
  • Open Source Packages
  • Machine Learning Interface: Benefits
  • Explicitly Specifying Oracle Database Parallelism
  • Embedded R Execution Initial Memory Management Considerations
  • Datastore: Benefits
  • Object Migration

Oracle Machine Learning with R Course Prerequisites:

• Proficiency in basic computer programming
• Knowledge of basic statistics concepts
• Familiarity with Oracle Database concepts and SQL
• Basic understanding of machine learning algorithms and models
• Prior exposure to R programming language
• Background in data manipulation and cleaning techniques.

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  • Familiar environment
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  • Team building
  • Immediate application

Training Exclusives

This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Certified Trainers.
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

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