Oracle/Deepdive into Machine Learning Using Autonomous Database

Deepdive into Machine Learning Using Autonomous Database Certification Training Course Overview

Enrol for the 2 days Oracle Machine Learning with Oracle Autonomous Database training and certification from Koenig Solutions accredited by Oracle. 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.

Target Audience:

  • Data scientists
  • Integration developers
  • Business users
  • Analysts

Learning Objectives:

  • Learn to use statistical functions to take advantage of Oracle Database.
  • Use Classification, Regression, and Attribute Importance algorithms for predictive analysis.
  • Identify unusual data by using Anomaly Detection and identify similar data by using Clustering algorithms.
  • Discover the probability of co-occurrence and extract smaller and richer sets of attributes.
  • Use a Time Series algorithm to forecast target values based on known history.

Deepdive into Machine Learning Using Autonomous Database (16 Hours) Download Course Contents

Live Virtual Classroom Fee On Request
Group Training
08 - 09 Nov GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 07 Dec 09:00 AM - 05:00 PM CST
(8 Hours/Day)

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4 Hours
8 Hours
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Course Modules

Module 1: 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
Module 2: 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)
Module 3: 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)
Module 4: Using Attribute Importance
  • Overview of attribute importance
  • List the types of attribute importance algorithms (Minimum Description Length, Principal Comp Analysis, CUR matrix decomposition)
Module 5: Implementing Anomaly Detection
  • Describe anomaly detection
  • Explain the anomaly detection algorithm (One-Class Support Vector Machines)
  • Discuss and recognize applicable use cases
Module 6: 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)
Module 7: Association Rules
  • Describe association rules
  • Explain transactional data
  • Discuss the Apriori algorithm, a type of association algorithm
Module 8: 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
Module 9: Using Time Series
  • Describe time series
  • Select a time series model
  • Explain time series statistics
  • Discuss Exponential Smoothing, a type of time series algorithm
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Course Prerequisites

Attended “Using Oracle Machine Learning with Autonomous Database” course Working knowledge of SQL and PL/SQL General understanding of statistics and probability