Oracle Machine Learning لـ Python على Oracle السحابة

Introduction to Machine Learning for Python

• Objectives
• Machine Learning: Introduction
• Introduction to Machine Learning
• Machine Learning: Process
• Machine Learning Process
• CRISP-DM Process
• Why Machine Learning?
• Machine Learning: Application Areas
• Use cases of Machine Learning
• Machine Learning: Use Cases
• Oracle Machine Learning: Use Cases
• Machine Learning: Workflow
• Simple Workflow for Machine Learning
• Machine Learning Algorithm: Types
• Types of Machine Learning Algorithms Supported in OML4Py
• Oracle Machine Learning for Python: Introduction
• Oracle Machine Learning for Python
• Oracle Machine Learning
• Oracle Machine Learning Notebooks
• Machine Learning for Python: Features
• Oracle Machine Learning for Python
• Machine Learning for Python: Advantages
• Advantages of Oracle Machine Learning for Python
• Oracle Machine Learning Notebooks
• Notebooks in Oracle Machine Learning
• Access Oracle Machine Learning Notebooks for Python
• Oracle Machine Learning Notebooks
• Python Libraries in OML4Py
• Python Libraries Included with OML4Py on ADB
• Summary

OML4Py Transparency Layer

• Objectives
• Transparency Layer: Overview
• Introduction to the OML4Py Transparency Layer
• Transparency Layer Data Table-Related Functions
• Transparency Layer Data Type Classes
• Python and SQL Data Type Equivalencies
• Push Local Python Data to the Database
• Push Local Python Data to the Database—Example
• Pull Data from the Database to a Local Python Session
• Create a Python Proxy Object for a Database Object
• Create a Persistent Database Table from a Python Data Set
• Transparency Layer Functions—Example
• Data Preparation
• Data Selection
• Data Selection by Column, Value—Example
• Combine Data
• Combine Two Objects
• Combine Two Objects—Example
• Clean Data
• Clean Data-Example
• Split Data
• Data Exploration
• Cross-Tabulate Data
• Cross-Tabulate Data—Example
• Mutate, Sort, and Summarize the Data
• Mutate, Sort, and Summarize Data
• Mutate the Data—Example
• Sort and Describe the Data—Example
• Summary

Working with Machine Learning Models

• Objectives
• Machine Learning Techniques and Algorithms
• OML4Py – In-Database Machine Learning Algorithms
• Machine Learning Classes and Algorithms
• Performance and scalability – model building
• Performance and scalability – data scoring
• Common in-database algorithm features
• Model settings
• Model Settings using GLM – Example
• Explanatory prediction details
• Partitioned model
• Structured data and unstructured text
• Automatic Data Preparation (ADP)
• Prediction (scoring)
• Attribute Importance
• Attribute Importance—Example
• Association Rules
• Association Rules—Example
• Association Rules – Example
• Decision Tree
• Decision Tree – Example
• Expectation Maximization
• Expectation Maximization – Example
• Explicit Semantic Analysis
• Explicit Semantic Analysis – Example
• Generalized Linear Model
• Generalized Linear Model – Example
• K-Means
• K-Means – Example
• Naive Bayes
• Naive Bayes – Example
• Neural Network
• Neural Network – Example
• Random Forest
• Random Forest – Example
• Singular Value Decomposition
• Singular Value Decomposition – Example
• Support Vector Machine
• Support Vector Machine – Example
• Create a Model Proxy Object from an Existing Model
• Create a Model from an Existing In-Database Model
• Export In-Database Models from Oracle Machine Learning for Python
• Export In-Database Models from OML4Py
• Export Oracle Machine Learning for Python Models
• Import a Model
• Import a Model – Example
• Summary

Data Store for Python Objects

• Objectives
• OML4Py Data Stores: Overview
• Introduction to OML4Py Data Store
• OML4Py Data Store
• OML4Py Interface for Data Store
• Save Objects to a Data Store
• Save Objects to a Data Store—Example
• Load Saved Objects from a Data Store
• Load Saved Objects from a Data Store—Example
• Get Information: Data Stores
• Get Information about Data Stores
• Get Information about Data Stores —Example
• Grant User Privileges on Data Stores
• Get Information: Data Store Objects
• Get Information about Data Store Objects
• Get Information about Data Store Objects—Example
• Delete Data Store Objects
• Delete Data Store Objects—Example
• Manage Access to Stored Objects
• Granting and Revoking Access to Data Stores —Example
• Summary

OML4Py Automated Machine Learning

• Objectives
• Oracle Automated Machine Learning: Overview
• Introduction to Oracle Automated Machine Learning
• Oracle Automated Machine Learning: Features
• OML AutoML Features
• Machine Learning Workflow: Automated by AutoML
• Automated Machine Learning (AutoML) Workflow
• Algorithm Selection
• Algorithm Selection—Example
• Feature Selection
• Feature Selection—Example
• Model Tuning
• Model Tuning—Example
• Model Selection
• Model Selection—Example
• Summary

Embedded Python Execution in OML4Py

• Objectives
• Embedded Python Execution: Introduction
• Introduction to Embedded Python Execution
• Embedded Python Execution
• Embedded Python Execution—Parallel Partitioned Data Flow Using Third-Party Package
• Run a Python Function
• Run a Python Function—Example
• Run a Python Function on the Specified Data
• Run a Python Function on Specified Data—Example
• Run a Python Function on Data: Grouped by Column Values
• Run a Python Function on Data—Grouped by Column Values
• Run a User-Defined Python Function on Sets of Rows
• Run a Python Function on Sets of Rows—Example
• Run a Python Function Multiple Times
• Run a Python Function Multiple Times—Example
• Script Repository: Overview
• Introduction to the OML4Py Script Repository
• Create and Store a Script
• Create and Store a Script—Example
• List Available Scripts
• List Available Scripts—Example
• Load and Drop Script from Repository
• Load and Drop a Script from Repository
• Load and Drop a Script from Repository—Example
• REST API : Introduction
• Introduction to REST API
• REST API –Workflow
• REST API Authentication—Example
• Create Data Frame and Load into DB
• Invoking the Function through REST API
• Creating and Invoking a user-defined Function through SQL API
• Summary

Working with cx_Oracle

• Objectives
• cx_Oracle: Overview
• Introduction to cx_Oracle
• cx_Oracle: Features
• Features of cx_Oracle
• cx_Oracle: Architecture
• cx_Oracle Architecture
• Oracle Database Connection – cx_Oracle
• Connect to Oracle Database
• Retrieving Data from Oracle Database
• Read/Write Table Methods
• Read and Write Table Methods—Example
• Invoking Functions and Stored Procedures
• Invoking Functions and Stored Procedures-cx_Oracle
• Invoking Functions and Stored Procedures—Example
• Calling a Stored Procedure with IN and OUT Parameters
• Summary