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July 21

15:00 UTC   Start Times Around the World


Machine Learning 101: Regression
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 third chapter of the series “Oracle Machine Learning Office Hours – Machine Learning 101”.

In this "ML Regression 101" we learned how to set up a data set for regression modeling, build machine learning models that predict numeric values such as home prices, evaluate model quality and compare algorithms, as well as use AutoML for Regression.

Video Highlights
01:01 Next session announcement: 102 Regression
02:01 Machine Learning 101 - Regression
03:07 What is Regression
05:31 History of Regression
08:00 Data for our example
10:30 Terminology
11:11 Data preparation
13:43 Linear Regression model intuition
19:35 Model evaluation
22:47 AutoML
23:12 Demo for Machine Learning 101: Regression
23:50 Data Exploration
33:53 Regression model build
38:12 Linear Regression: Predicted vs. Actuals and Residuals
40:02 Computing the goodness-of-fit statistics in OML4Py
42:08 Linear Regression with feature creation and feature selection
43:57 Support Vector Machines Regression
47:35 Neural Networks Regression
49:25 Ranking of models so far
49:40 AutoML algorithm selection
51:04 AutoML GLM Ridge
52:37 Final ranking of models
53:20 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.