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May 19

16:00 UTC   Start Times Around the World

Machine Learning 101: Classification
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?

In this special series “Oracle Machine Learning Office Hours – Machine Learning 101” we went 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.

This first session in the series covered Classification, where we learned how to set up a data set for classification modeling, build machine learning models that can, e.g., discern between good or bad customers for a marketing offer, and evaluate the quality of that model.

Video Highlights
00:48 Next session announcement: 101 Regression
01:16 Web Questions: Bias in machine learning
08:32 Machine Learning 101 - Classification
09:38 What is Machine Learning
10:59 Machine Learning process - CRISP-DM
12:34 What is Classification?
15:44 What type of data is needed for a Classification problem?
17:24 Machine Learning terminology
20:45 Data preparation
29:12 Confusion Matrix for model evaluation
37:27 AutoML with OML4Py
38:04 Demo Machine Learning 101 - Classification
38:30 Data loading and processing
41:30 Random sampling using OML4Py
43:20 Data visualization with MatPlot Lib with OML4Py
45:07 Split into Train and Test
46:34 "Gut Feeling" classification "selection"
47:53 Decision Tree models
48:43 Naive Bayes model
49:20 Logistic Regression models
50:04 Random Forest models
50:32 Support Vector Machine models
51:22 Neural Networks models
52:05 AutoML models
54:26 Roadmap: AutoML User Interface
54:56 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.