data-science-and-ml content update

Author: vatudorache

data-platform/data-science-and-ml/oracle-graph-for-data-science/README.md

@@ -0,0 +1,30 @@
+# Oracle Graph for Data Science
+
+This page is covering Oracle Graph analytics for data science projects.
+
+Graph databases, part of Oracle’s converged database offering, eliminate the need to set up a separate database and move data. Analysts and developers can perform fraud detection in banking, find connections and link to data, and improve traceability in smart manufacturing, all while gaining enterprise-grade security, ease of data ingestion, and strong support for data workloads.
+
+## Table of Contents
+ 
+1. [Useful Links](#useful-links)
+2. [Reusable Assets](#reusable-assets)
+
+## Useful Links
+
+- [Oracle Graph homepage](https://www.oracle.com/database/graph/)
+- [Oracle Graph on YouTube](https://www.youtube.com/playlist?list=PL3ZqpALcm8HMR7nHRlpA2pXiUz99SMO5Z)
+- [Oracle Graph on Oracle Blogs](https://blogs.oracle.com/search.html?q=graph)
+- [Oracle Graph on GitHub](https://github.com/oracle-samples/pgx-samples)
+- [Oracle Graph on Oracle LiveLabs](https://apexapps.oracle.com/pls/apex/f?p=133:100:2717775602397::::SEARCH:graph)
+- [Oracle Graph documentation](https://docs.oracle.com/en/database/oracle/oracle-database/23/graph.html)
+
+## Reusable Assets
+
+
+# License
+
+Copyright (c) 2023 Oracle and/or its affiliates.
+
+Licensed under the Universal Permissive License (UPL), Version 1.0.
+
+See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/folder-structure/LICENSE) for more details.

data-platform/data-science-and-ml/oracle-machine-learning/README.md

@@ -0,0 +1,30 @@
+# Oracle Machine Learning
+
+This page is covering Oracle Machine Learning, or Machine Learning in Oracle Database.
+
+Machine Learning in Oracle Database supports data exploration, preparation, and machine learning modeling at scale using SQL, R, Python, REST, automated machine learning (AutoML), and no-code interfaces. It includes more than 30 high performance in-database algorithms producing models for immediate use in applications. By keeping data in the database, organizations can simplify their overall architecture and maintain data synchronization and security. It enables data scientists and other data professionals to build models quickly by simplifying and automating key elements of the machine learning lifecycle.
+
+## Table of Contents
+ 
+1. [Useful Links](#useful-links)
+2. [Reusable Assets](#reusable-assets)
+
+## Useful Links
+
+- [Oracle Machine Learning homepage](https://www.oracle.com/artificial-intelligence/database-machine-learning/)
+- [Oracle Machine Learning on YouTube](https://www.youtube.com/playlist?list=PLdtXkK5KBY57_y3Z0SW2cbCqGUPbfc94w)
+- [Oracle Machine Learning on Oracle Blogs](https://blogs.oracle.com/machinelearning/)
+- [Oracle Machine Learning on GitHub](https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning)
+- [Oracle Machine Learning on Oracle LiveLabs](https://apexapps.oracle.com/pls/apex/f?p=133:100:14606520475441::::SEARCH:oracle+machine+learning)
+- [Oracle Machine Learning documentation](https://docs.oracle.com/en/database/oracle/machine-learning/)
+
+## Reusable Assets
+
+
+# License
+
+Copyright (c) 2023 Oracle and/or its affiliates.
+
+Licensed under the Universal Permissive License (UPL), Version 1.0.
+
+See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/folder-structure/LICENSE) for more details.