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.