Oracle/Deepdive into Machine Learning Using Autonomous Database

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Deepdive into Machine Learning Using Autonomous Database 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.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

The 1-on-1 Advantage

Methodology

Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

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
  • 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)
  • Overview of attribute importance
  • List the types of attribute importance algorithms (Minimum Description Length, Principal Comp Analysis, CUR matrix decomposition)
  • Describe anomaly detection
  • Explain the anomaly detection algorithm (One-Class Support Vector Machines)
  • Discuss and recognize applicable use cases
  • 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)
  • Describe association rules
  • Explain transactional data
  • Discuss the Apriori algorithm, a type of association algorithm
  • 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
  • Describe time series
  • Select a time series model
  • Explain time series statistics
  • Discuss Exponential Smoothing, a type of time series algorithm
Live Online Training (Duration : 16 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites

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

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FAQ's


In both, you choose the schedule. In public, other participants can join, Private other participants want to join.
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Yes, we also offer weekend classes.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
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