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June 09

16:00 UTC   Start Times Around the World

OML Office Hours – Machine Learning 102: 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? Join us for this second chapter of the series “Oracle Machine Learning Office Hours – Machine Learning 101”.

In this "ML Classification 102" session we picked up where we left off from our 101 Session, and went deeper in our discussions on ML algorithms, the importance of Feature Selection, and explored even more the correct way to evaluate models using the Confusion Matrix and the many statistics that can be computed from it.

We continued to make use of Oracle Machine Learning Notebooks, with Python and SQL as the underlying languages and OML4Py with AutoML for our demo environment.

Video Highlights
01:01 Web Questions
01:30 Next session announcement: 101 Regression
02:21 Machine Learning 102 - Classification
03:30 Review on model evaluation
06:11 Confusion Matrix
07:05 Lift/Gains Chart
19:57 Decile lift chart using Python in OML4Py
22:35 Density of Predictions compared using Python in OML4Py
26:10 ROC curve using Python in OML4Py
30:49 Demo Machine Learning 102 - Classification
32:15 Data loading and processing
34:00 Python function to automate OML4Py model build
38:13 Python function to automate OML4Py model evaluation
47:05 Decision Tree model evaluation
49:31 Naive Bayes model evaluation
50:00 Logistic Regression model evaluation
50:42 Logistic Regression (with feature generation and selection) model evaluation
51:35 Random Forest model evaluation
52:36 Support Vector Machines model evaluation
53:32 Neural Networks model evaluation
54:09 AutoML
54:40 Final summary of all models
55:32 Q&A

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Experts

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.