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November 05

16:00 UTC   Start Times Around the World

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Description

Machine Learning 102: 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.

This sixth session in the series covered Clustering 102, where we learn more about the methods on multiple dimensions, how to compare Cluster techniques, and explore Dimensionality Reduction and how to extract only the most meaningful attributes from datasets with lots of attributes (or derived attributes).

Session highlights:
00:39 Oracle Machine Learning Office Hours - next Session
01:28 Machine Learning 102 - Clustering
02:00 Clustering 102 - Demo Introduction
03:25 Dataset for Demo
03:45 Dataset view
04:40 2-D visualization of subset of Dataset
06:40 k-Means model demo build with k=2 clusters
08:10 k-Means cluster prediction
09:15 k-Means prediction visualization in 2-D and 3-D
11:20 Identify Attributes that explain the prediction for cluster
13:15 Elbow Method to identify ideal number of clusters for k-Means
15:45 Function to build, score and plot k-Means clusters
16:49 Visualizing k-Means from k=2 to k=7 in 3-D
18:05 O-Cluster algorithm demo
21:45 Materializing OML4Py proxy object to a Database table
23:21 O-Cluster model build using PL/SQL
24:55 O-Cluster model settings and attributes
25:45 O-Cluster model views
27:52 O-Cluster cluster prediction
29:20 O-Cluster prediction visualization in 2-D and 3-D
32:00 Expectation-Maximization clustering demo introduction
33:10 E-M clustering model build
37:43 E-M clustering scoring and Attribute Importance
38:13 Identify Attributes that explain the prediction for cluster
39:14 Create a function to build, score and plot an E-M clustering model
39:38 Several E-M clustering results using several available settings
41:20 E-M clustering with Model Search Enable
45:19 E-M clustering models and model views
47:10 E-M clustering scoring via Python and SQL
50:55 Q&A

Your Experts

Marcos Arancibia
Marcos Arancibia, Senior Principal Product Manager, Machine Learning    
Marcos Arancibia is the Product Manager for Oracle Machine Learning, working with Machine Learning in the Oracle Database and on Spark. He develops product strategy, roadmap prioritization, product positioning and product evangelization, helping define the product roadmap for Oracle Machine Learning. Before joining Oracle in 2010 he spent 13 years at SAS Institute Inc., from Country Manager in LAD to Regional Data Mining lead in the US. He holds a bachelor's degree with additional courses in the master's degree, both in Statistics from UNICAMP in Brazil. He has Certifications from Stanford on AI and Machine Learning, and from the University of Washington on Computational Neuroscience.
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.

All Sessions

November 2 2021 15:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
October 12 2021 15:00:00 UTCOML feature highlight: Time Series analysis with Oracle Machine Learning
October 5 2021 15:00:00 UTCOML4Py: Using third-party Python packages from Python, SQL and REST
September 28 2021 15:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
September 21 2021 15:00:00 UTCOML usage highlight: Live Demo of Oracle Stream Analytics with OML AutoML UI and OML Services
August 17 2021OML Usage Highlight: ML on SailGP data: Predicting the best sailing direction
August 10 2021OML feature highlight: Deploy an XGBoost Model using OML Services
August 3 2021ML Concepts - Using Cross-Validation with OML in-Database and with Embedded Python Execution
June 29 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn
June 22 2021ML Concepts - Encoding of Categorical Attributes: OneHot vs Mean vs WoE and when to use them
June 15 2021OML usage highlight: Machine Learning Recommendations for Maintenance and Repair
May 25 2021Hands-On Lab using Oracle Machine Learning AutoML UI on Autonomous Database
May 18 2021Hands-On Lab using Oracle Machine Learning Services on Autonomous Database
May 11 2021OML usage highlight: Oracle Process Automation with Real-time OML Services scoring
April 20 2021OML usage highlight: Oracle Stream Analytics with Real-time OML Services scoring
April 13 2021OML usage highlight: Making Oracle Digital Assistant smarter with OML Services
March 30 2021OML feature highlight: OML AutoML UI for Automated Model Building
March 23 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn
March 11 2021OML feature highlight: OML Services on Autonomous for Model Deployment
March 2 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn