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Oracle Machine Learning Office Hours

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August 04

15:00 UTC   Start Times Around the World


Machine Learning 102: 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 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.

In this fourth session in the series we covered Regression 102, with a look at multiple input attributes, attribute selection, feature generation, and a deeper look into diagnosis and potential problems

Video Highlights
00:39 Next Session Announcement: 101 Clustering
01:27 Today's Session: Machine Learning 102
04:10 Demo Part I of IV - Data Preparation
12:15 Sampling Data for using MatPlot Lib with OML4Py
16:10 How to identify the actual attributes that make sense to use
19:55 Data Cleaning
20:50 Data Transformation
23:24 Demo Part II of IV - Data Analysis
23:39 Random sampling for MatPlot Lib visualizations
28:25 Split data into Train and Test
29:40 Demo Part III of IV - Model Building
30:45 Linear Regression
41:00 Support Vector Machines
41:22 Neural Networks
41:50 List of Top Models for prediction
42:44 Demo Part IV of IV - AutoML
43:28 AutoML algorithm selection
44:13 AutoML Neural Networks
46:57 AutoML SVM
47:40 Final ranking of model quality
48:49 AutoML Neural Networks: Predicted vs. Actuals and Residuals
50:00 Machine Learning eXplainability for AutoML Neural Network
51:00 Visualizations in 3-D
52:40 Final conclusions
55:05 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.