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Oracle Machine Learning Office Hours

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September 29

15:00 UTC   Start Times Around the World

Description

Machine Learning 101: Clustering
Have you always been curious about what machine learning can do for your business problem, but could never find the time to learn the practical necessary skills? Do you wish to learn what Classification, Regression, Clustering and Feature Extraction techniques do, and how to apply them using the Oracle Machine Learning family of products?

Join us for this special series “Oracle Machine Learning Office Hours – Machine Learning 101”, where we will go through the main steps of solving a Business Problem from beginning to end, using the different components available in Oracle Machine Learning: programming languages and interfaces, including Notebooks with SQL, UI, and languages like R and Python.

Our fifth session in the series covered Clustering 101, where we learned the terminology around Clustering or Segmentation, how to get the data prepared for clustering, how to measure cluster separation, identify potential pitfalls and use cases.

We continued making use of the Oracle Machine Learning for Python (OML4Py) interface for the Autonomous Database on our Demo.

Video Highlights
01:47 Machine Learning 101 - Clustering
02:45 What is Clustering?
04:40 Clustering Algorithms and Methods
07:49 Types of data needed for Clustering
09:30 Workflow and Data Preparation
14:05 Data used in the Demo
14:50 Clustering Model Intuition for k-Means
22:24 k-Means properties
25:53 Features of the Oracle Machine Learning clustering algorithms
28:22 Demo of Machine Learning 101: Clustering
30:24 Demo: selecting subset of data and filtering outliers
32:35 Demo: k-Means model with k=2
36:10 Demo: k-Means model prediction
38:45 Demo: Create a function for building, scoring and plotting k-Means
40:10 Demo: k-Means testing from k=2 to k=7
42:00 Demo: Expectation-Maximization
46:42 Demo: Create a function for building, scoring and plotting Expectation-Maximization
48:00 Demo: Expectation-Maximization testing with max clusters from 2 to 7
51:40 Q&A

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Your Experts

Marcos Arancibia
Marcos Arancibia, Product Manager, Data Science and Big Data    
Marcos Arancibia is the Product Manager for Oracle Data Science and Big Data. He works with Machine Learning in the Oracle Database and on Big Data clusters under Hadoop and Spark, on premises and in the Oracle Cloud. He works within Product Management to develop product strategy, roadmap prioritization, product positioning and product evangelization, working closely with the engineering team in defining the product roadmaps for Oracle Machine Learning and Big Data in the Cloud. Before joining Oracle 9 years ago he was at SAS Institute Inc. for 13 years as a Data Mining architect and expert in the US and Latin America. He holds a Bachelor Degree of Science in Statistics with additional courses in the Master of Science in Statistics, both from UNICAMP in Brazil. He has Certifications from Stanford on AI and Machine Learning, and from the University of Washington on Computational Neuroscience. He is an expert on Deep Learning and passionate about Machine Learning.
Mark Hornick
Mark Hornick, Senior Director, Product Management, Data Science and Machine Learning    
Mark Hornick is the Senior Director of Product Management for the Oracle Machine Learning (OML) family of products. He leads the OML PM team and works closely with Product Development on product strategy, positioning, and evangelization, Mark has over 20 years of experience with integrating and leveraging machine learning with Oracle technologies, working with internal and external customers in the application of Oracle’s machine learning technologies for scalable and deployable data science projects. Mark is Oracle’s representative on the R Consortium’s Board of Directors, an Oracle Adviser and founding member of the Business Intelligence Warehousing and Analytics (BIWA) User Community, and Content Selection Committee Chair for the Analytics and Data Summits.