Deepdive into Maching Learning Usging Autonomous Database

Cost $2700

Course Code

2 days

Live Virtual Class

What you will learn

This training 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.


In this course, you will learn about:

  • 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


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


  • Data scientists
  • Integration developers
  • Business users
  • Analysts


  • 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.



  • 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)


  • 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

Enroll for Deepdive into Machine Learning Using Autonomous Database

Deep Dive into Machine Learning Using Autonomous Database


SKU: D107440GC10 Categories: ,

Additional information


May 17 2021 – May 18 2021 (2 days) 9:00AM – 5:00PM US-Pacific, Sep 20 2021 – Sep 21 2021 (2 days) 9:00AM – 5:00PM US-Pacific, Jan 18 2022 – Jan 19 2022 (2 days) 9:00AM – 5:00PM US-Eastern


Live Virtual Class


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