Oracle Machine Learning with Autonomous Database Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

In Oracle Machine Learning with Autonomous Database - Essentials training course, you will learn to provision and use Oracle Autonomous Database and Analytics Cloud service instances with Oracle Machine Learning. Experience the ease of performing predictive analysis of data on Oracle Cloud.

The Oracle Machine Learning with Autonomous Database - Advanced training course enables you to use Oracle Machine Learning with Oracle Autonomous Database so that you can implement Predictive Analysis. This is a great starting point for data scientists, developers, business users, and anyone who wants to learn about the algorithms and key features of Oracle Machine Learning.

By attending Oracle Machine Learning with Autonomous Database - Essentials workshop, delegates will learn to:

Describe the key features of Oracle Machine Learning
Create Projects and workspaces, and manage users in Oracle Machine Learning
Crete and run Oracle Machine Learning Notebooks
Develop SQL Scripts that can be used in Notebooks
Create Notebooks for data analysis and Data Visualization
Collaborate and share Notebooks with other Oracle Machine Learning users
Schedule Jobs to run Notebooks
Use Analytics Cloud to create data visualizations
Extract data for analytics from Autonomous Data Warehouse and Autonomous Transaction Processing Cloud service instances
Use Machine Learning feature in Oracle Analytics Cloud

By attending Oracle Machine Learning with Autonomous Database - Advanced workshop, delegates will learn :

The creation of notebooks, projects, workspaces, and assigning workspace permissions to users
How to develop SQL Scripts and running SQL commands in a paragraph of OML
Statistical functions to make use of Oracle Database
Different machine learning algorithms like the Classification Model, the types of classification algorithms, regression, and building a use case
Attribute Importance, Anomaly Detection, Clustering, Association Rules, Feature Extraction, and Time Series along with the use cases

For Oracle Machine Learning with Autonomous Database - Essentials

Working knowledge of SQL and PL/SQL
General understanding of statistics and probability

For Oracle Machine Learning with Autonomous Database - Advanced

Attend a training on Oracle Machine Learning with Autonomous Database - Essentials or have equivalent knowledge

For Oracle Machine Learning with Autonomous Database - Essentials

Analysts
Data Scientists
Managers

For Oracle Machine Learning with Autonomous Database - Advanced

Data Scientist
Integration developers
Business users
Analyst

Introduction to Oracle Machine Learning and Oracle Autonomous Cloud Platform

Growth of Oracle Machine Learning
Oracle Machine Learning: Features & Components
Compatible Oracle Cloud Services
Oracle Cloud Platform Autonomous Services
Oracle Autonomous Database
Oracle Analytics Cloud
Autonomous Data Warehouse Cloud: Use Cases

Creating Workspace and Projects in Oracle Machine Learning

Accessing the Oracle Machine Learning Home Page
Creating a New Project and Workspace
Managing Workspaces Permissions

Creating SQL Scripts and Running SQL Commands in Oracle Machine Learning

SQL Script Scratchpad
Developing & Running SQL Scripts
Exporting and Importing SQL Scripts
Restrictions on SQL Commands
Restrictions for Database Options
Restrictions for Database Initialization Parameters
Connection Groups

Notebooks in Oracle Machine Learning

Examples: OML Notebook
Creating & Editing a Notebook
Typical Workflow for Analyzing Data with Oracle Machine Learning
Traditional Programming Versus Machine Learning
Creating Text Input Forms in Notebooks
Creating Select Forms in Notebooks
Creating Check Box Forms in Notebooks
Setting Up an Output Format

Collaborating Using Templates in Oracle Machine Learning

Templates in Oracle Machine Learning
Saving a Notebook as Template in Oracle Machine Learning
Sharing Templates
Editing Template Settings
Collaborate Using the Export Option

Working with Jobs in Oracle Machine Learning

Jobs in Oracle Machine Learning
Creating a Job
Viewing Job Logs

Administering Oracle Machine Learning

Workflow for Managing Oracle Machine Learning
User Data
Compute Resource
Creating User Accounts for Oracle Machine Learning

Working with Oracle Machine Learning using Autonomous Transaction Processing Cloud

Autonomous Transaction Processing
Typical Workflow for Autonomous Transaction Processing, and Other Services and Tools
Using Oracle Machine Learning with Autonomous Transaction Processing
K-means Algorithm
Oracle Machine Learning for Data Access, Analysis, and Discovery

Creating Visualizations in Oracle Machine Learning Using Analytics Cloud

Oracle Analytics Cloud
Creating Connections to Oracle Autonomous Data Warehouse Cloud
Create Connections to Autonomous Transactional Processing Cloud
Use Machine Learning to Analyze Data by Using Oracle Analytics Cloud
Using Data Flow to Curate Data Sources
Running Data Flows
Prediction Visualization

Overview

Overview of the topics covered
List the prerequisites for this course
Describe the schedule of the course

Using Statistical Functions

An overview of statistical functions
List the advantages of performing statistical functions inside the database
Explain the descriptive statistics supported inside the database
Describe hypothesis testing and work through some examples
Describe correlation analysis and work through some examples
Describe cross-tabulations and work through some examples

Classification Model

Overview of classification modeling
Describe the testing of a classification model
Describe biasing a classification model
List the types of classification algorithms (Decision Tree, Naive Bayes, Generalized Linear Models, Random Forest, Support Vector Machines, Neural Network, MSET-SPRT, XGBoost)

Regression

Describe regression modeling
Describe the testing of a regression model
List the types of regression algorithms (Generalized Linear Models, Neural Network, Support Vector Machines)

Using Attribute Importance

Overview of attribute importance
List the types of attribute importance algorithms (Minimum Description Length, Principal Comp Analysis, CUR matrix decomposition)

Implementing Anomaly Detection

Describe anomaly detection
Explain the anomaly detection algorithm (One-Class Support Vector Machines)
Discuss and recognize applicable use cases

Using Clustering

Describe clustering
Explain hierarchical clustering
Discuss how to evaluate a clustering model
List the types of clustering algorithms (Expectation Maximization, k-Means, Orthogonal Partitioning Clustering)

Association Rules

Describe association rules
Explain transactional data
Discuss the Apriori algorithm, a type of association algorithm

Using Feature Selection and Extraction

Describe feature selection
Describe feature extraction
List the types of feature extraction algorithms:

Explicit Semantic Analysis
Non-Negative Matrix Factorization
Singular Value Decomposition
Prediction Component Analysis

Using Time Series

Describe time series
Select a time series model
Explain time series statistics
Discuss Exponential Smoothing, a type of time series algorithm

Encarta Labs Advantage

One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
All courses are delivered by Industry Veterans
Get jumpstarted from newbie to production ready in a matter of few days

Trained more than 50,000 Corporate executives across the Globe
All our trainings are conducted in workshop mode with more focus on hands-on sessions

Oracle Machine Learning with Autonomous Database

COURSE AGENDA

Course Overview