Explanation
Jobs allow you to schedule the running of notebooks. In the Jobs page, you can create jobs, duplicate jobs, start and stop jobs, delete jobs, and monitor job status by viewing job logs, which are read-only notebooks.
About Jobs. The Jobs page lists all the jobs created, along with the job name, notebook, owner of the job, last start date, next run date, status, and schedule. Create Jobs to Schedule Notebooks You can create jobs to schedule your notebook with preferred scheduling set-tings. View Job Logs You can view the historical logs of any particular job in the Job Log interface. About Jobs The Jobs page lists all the jobs created, along with the job name, notebook, owner of the job, last start date, next run date, status, and schedule.

* Oracle Analytics Cloud and Oracle Autonomous Database are two Oracle Cloud services that are integrated with Oracle Machine Learning12.
* Oracle Analytics Cloud is a comprehensive cloud service that provides a unified platform for data preparation, analysis, visualization, and machine learning. It supports Oracle Machine Learning Notebooks, which enable data scientists and other data professionals to work collaboratively using SQL, PL/SQL, Python, and R1.
* Oracle Autonomous Database is a self-driving, self-securing, and self-repairing cloud service that supports various workloads such as data warehousing, transaction processing, and JSON document storage. It includes Oracle Machine Learning Services, which enable users to build, deploy, and manage machine learning models using REST APIs2.

Explanation
Oracle Machine Learning Notebooks Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Note-books support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. View interactive product tour Oracle Machine Learning Notebooks with Autonomous Data Warehouse documentation Oracle Machine Learning Notebooks with Autonomous Transaction Processing documentation Oracle Ma-chine Learning Notebooks with Autonomous JSON Database documentation

* Unsupervised machine learning is a type of machine learning in which algorithms learn patterns exclusively from unlabeled data34. Unsupervised learning algorithms discover hidden structures or groupings in the data without any supervision or guidance from human experts34.
* Three statements that are true about unsupervised machine learning are:
* There is no previously known result to guide the algorithm in building the model. Unsupervised learning algorithms do not have any predefined target variable or outcome to optimize. They rely on the intrinsic properties of the data to find meaningful patterns or clusters34.
* It can be used as a preliminary step for supervised algorithms. Unsupervised learning algorithms can be useful for exploratory data analysis, feature extraction, dimensionality reduction, or data preprocessing before applying supervised learning algorithms34.
* It uses unlabeled data. Unsupervised learning algorithms do not require any labeled data or annotations to learn from. They can work with raw or unstructured data such as text, images, audio, or video34.

Explanation
Oracle Machine Learning Notebooks is an Apache Zeppelin-based collaborative web-based inter-face that provides a development environment to create machine learning notebooks where you can perform data exploration and visualizations, data preparation and machine learning.
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-le