Deepdive into Machine Learning Using Autonomous Database Course Overview

Deepdive into Machine Learning Using Autonomous Database Course Overview

The "Deepdive into Machine Learning Using Autonomous Database" course is a comprehensive learning path designed for individuals looking to harness the power of machine learning (ML) within an autonomous database environment. This course covers a wide range of topics, starting with statistical functions and their advantages when performed inside the database. Learners will gain hands-on experience with descriptive statistics, hypothesis testing, correlation analysis, and cross-tabulations.

As the course progresses, participants will delve into various ML techniques, including classification modeling, regression, and anomaly detection, with practical examples and use cases. They will explore different algorithms like Decision Trees, Naive Bayes, and Neural Networks, and learn how to test and bias models for improved outcomes.

Modules on attribute importance, clustering, association rules, feature selection and extraction, and time series analysis will equip learners with the skills to identify key features, cluster data, uncover hidden patterns, and predict future trends. By the end of the course, participants will be well-versed in leveraging in-database ML capabilities to solve complex data-driven problems, making them valuable assets in today's data-centric industries.

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  • Live Online Training (Duration : 16 Hours)
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  • Live Online Training (Duration : 16 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure that participants are adequately prepared and can derive maximum benefit from the "Deepdive into Machine Learning Using Autonomous Database" course, the following minimum prerequisites are recommended:


  • Basic understanding of database concepts, including tables and relationships.
  • Familiarity with SQL (Structured Query Language) to perform data queries and manipulations.
  • Introductory knowledge of statistics, including measures of central tendency and dispersion.
  • General awareness of the concepts of Machine Learning, such as what it entails and its common applications.
  • Comfort with mathematical concepts, particularly algebra and probability, to grasp algorithmic underpinnings.
  • Basic programming experience, preferably in Python, R, or a similar language used in data analysis and machine learning.
  • An analytical mindset and problem-solving skills to follow through with hands-on examples and exercises.

These prerequisites are designed to establish a foundation upon which the course content can build. Students without prior experience in these areas are encouraged to seek introductory courses or resources to familiarize themselves with these subjects before embarking on this in-depth machine learning training.


Target Audience for Deepdive into Machine Learning Using Autonomous Database

Koenig Solutions' Deepdive into Machine Learning Using Autonomous Database course is tailored for professionals seeking advanced analytics skills.


  • Data Scientists and Machine Learning Engineers
  • Database Administrators interested in analytics
  • Data Analysts seeking to upgrade to machine learning roles
  • IT Professionals wanting to specialize in machine learning
  • Software Developers looking to implement machine learning in applications
  • Business Intelligence Professionals
  • Statisticians transitioning to machine learning roles
  • Research Scientists in fields requiring data analysis
  • Academic Professionals and Students in computer science or data science fields
  • AI Enthusiasts looking to understand machine learning algorithms deeply


Learning Objectives - What you will Learn in this Deepdive into Machine Learning Using Autonomous Database?

Introduction to Learning Outcomes and Concepts:

This course provides a comprehensive exploration into the practical applications of Machine Learning using an Autonomous Database, enhancing your ability to perform complex statistical analyses, develop predictive models, and extract valuable insights from data.

Learning Objectives and Outcomes:

  • Understand and apply various statistical functions within the database to analyze and interpret data effectively.
  • Identify the benefits of conducting statistical operations inside the database for performance and scalability.
  • Gain proficiency in descriptive statistics and hypothesis testing to validate data-driven hypotheses.
  • Learn to perform and interpret correlation analysis and cross-tabulations for deeper data insights.
  • Acquire knowledge of different classification algorithms and their applications, including how to test and bias models.
  • Develop skills in regression modeling and become familiar with various regression algorithms for predicting continuous outcomes.
  • Understand the concept of attribute importance and learn how to apply algorithms to identify significant data features.
  • Learn the principles of anomaly detection, its algorithmic approaches, and how to apply them to real-world scenarios.
  • Gain expertise in clustering techniques and learn how to evaluate models to segment data into meaningful groups.
  • Explore association rules and transactional data to uncover relationships between items in large datasets.
  • Understand feature selection and extraction to improve model performance and learn algorithms for effective dimensionality reduction.
  • Develop an understanding of time series analysis, including model selection, time series statistics, and exponential smoothing algorithms.