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Intro to H2O Machine Learning in Python - Galvanize Seattle
The document provides an overview of h2o.ai, an open-source machine learning platform founded in 2012 with a focus on ease of use and scalability for big data applications. It details the h2o software features, the founders' credentials, and the various machine learning algorithms and frameworks supported by the platform. Additionally, it outlines resources for learning more about h2o, including online training, video presentations, and community events.
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Intro to H2O Machine Learning in Python - Galvanize Seattle
- 1. H2O.ai Machine Intelligence Intro toH2O Machine Learning in Python Erin LeDell Ph.D. Seattle Galvanize February 2016
- 2. H2O.ai Machine Intelligence Introduction • Statistician& Machine Learning Scientist at H2O.ai in Mountain View, California, USA • Ph.D. in Biostatistics with Designated Emphasis in Computational Science and Engineering from UC Berkeley (focus on Machine Learning) • Worked as a data scientist at several startups • Written several machine learning software packages
- 3. H2O.ai Machine Intelligence H2O.ai H2O Company H2OSoftware • Team: 50. Founded in 2012, Mountain View, CA • Stanford Math & Systems Engineers • Open Source Software • Ease of Use via Web Interface • R, Python, Scala, Spark & Hadoop Interfaces • Distributed Algorithms Scale to Big Data
- 4. H2O.ai Machine Intelligence H2O.ai Founders SriSatishAmbati • CEO and Co-founder at H2O.ai • Past: Platfora, Cassandra, DataStax, Azul Systems, UC Berkeley • CTO and Co-founder at H2O.ai • Past: Azul Systems, Sun Microsystems • Developed the Java HotSpot Server Compiler at Sun • PhD in CS from Rice University Dr. Cliff Click
- 5. H2O.ai Machine Intelligence Scientific AdvisoryCouncil Dr. Trevor Hastie Dr. Rob Tibshirani Dr. Stephen Boyd • John A. Overdeck Professor of Mathematics, Stanford University • PhD in Statistics, Stanford University • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Co-author with John Chambers, Statistical Models in S • Co-author, Generalized Additive Models • 108,404 citations (via Google Scholar) • Professor of Statistics and Health Research and Policy, Stanford University • PhD in Statistics, Stanford University • COPPS Presidents’ Award recipient • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Author, Regression Shrinkage and Selection via the Lasso • Co-author, An Introduction to the Bootstrap • Professor of Electrical Engineering and Computer Science, Stanford University • PhD in Electrical Engineering and Computer Science, UC Berkeley • Co-author, Convex Optimization • Co-author, Linear Matrix Inequalities in System and Control Theory • Co-author, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- 6. H2O.ai Machine Intelligence Agenda • H2OPlatform • H2O Python module • EEG Python Notebook Demo
- 7. H2O.ai Machine Intelligence H2O Platform Part1 of 3 Intro to H2O in Python
- 8. H2O.ai Machine Intelligence H2O Software H2Ois an open source, distributed, Java machine learning library. APIs are available for: R, Python, Scala & REST/JSON
- 9. H2O.ai Machine Intelligence H2O SoftwareOverview Speed Matters! No Sampling Interactive UI Cutting-Edge Algorithms • Time is valuable • In-memory is faster • Distributed is faster • High speed AND accuracy • Scale to big data • Access data links • Use all data without sampling • Web-based modeling with H2O Flow • Model comparison • Suite of cutting-edge machine learning algorithms • Deep Learning & Ensembles • NanoFast Scoring Engine
- 10. H2O.ai Machine Intelligence Current AlgorithmOverview Statistical Analysis • Linear Models (GLM) • Naïve Bayes Ensembles • Random Forest • Distributed Trees • Gradient Boosting Machine • R Package - Super Learner Ensembles Deep Neural Networks • Multi-layer Feed-Forward Neural Network • Auto-encoder • Anomaly Detection • Deep Features Clustering • K-Means Dimension Reduction • Principal Component Analysis • Generalized Low Rank Models Solvers & Optimization • Generalized ADMM Solver • L-BFGS (Quasi Newton Method) • Ordinary Least-Square Solver • Stochastic Gradient Descent Data Munging • Scalable Data Frames • Sort, Slice, Log Transform
- 11. H2O.ai Machine Intelligence Distributed Key ValueStore H2O Frame H2O Distributed Computing • Multi-node cluster with shared memory model. • All computations in memory. • Each node sees only some rows of the data. • No limit on cluster size. • Objects in the H2O cluster such as data frames, models and results are all referenced by key. • Any node in the cluster can access any object in the cluster by key. • Distributed data frames (collection of vectors). • Columns are distributed (across nodes) arrays. • Each node must be able to see the entire dataset (achieved using HDFS, S3, or multiple copies of the data if it is a CSV file). H2O Cluster
- 12. H2O.ai Machine Intelligence H2O onAmazon EC2 H2O can easily be deployed on an Amazon EC2 cluster. The GitHub repository contains example scripts that help to automate the cluster deployment.
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- 14. H2O.ai Machine Intelligence H2O forPython Part 2 of 3 Intro to H2O in Python
- 15. H2O.ai Machine Intelligence Design h2o Pythonmodule • Java 7 or later. • Python 2 or 3. • A few Python module dependencies. • Linux, OS X or Windows. • The easiest way to install the “h2o” Python module is pip. • Latest version: http://h2o.ai/download • No computation is ever performed in Python. • All computations are performed in highly optimized Java code in the H2O cluster and initiated by REST calls from Python. Requirements Installation
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- 17. H2O.ai Machine Intelligence Start H2OCluster from Python
- 18. H2O.ai Machine Intelligence Start H2OCluster from Python
- 19. H2O.ai Machine Intelligence Train amodel (e.g. GBM)
- 20. H2O.ai Machine Intelligence Inspect ModelPerformance
- 21. H2O.ai Machine Intelligence EEG Demo Part3 of 3 Intro to H2O in Python
- 22. H2O.ai Machine Intelligence EEG forEye Detection Problem Data • Goal is to accurately predict the eye state using minimal, surface level EEG data. • Binary outcome: Open vs Closed • Data from Emotiv Neuralheadset. • Predictor variables describe signals from 14 EEG channels placed on the surface of the head. Source: http://archive.ics.uci.edu/ml/datasets/EEG+Eye+State
- 23. H2O.ai Machine Intelligence EEG Datain H2O Flow
- 24. H2O.ai Machine Intelligence EEG Datain H2O Python
- 25. H2O.ai Machine Intelligence H2O PythonDemo https://github.com/h2oai/h2o-3/blob/master/ h2o-py/demos/H2O_tutorial_eeg_eyestate.ipynb For comparison, there is scikit-learn version: https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/ EEG_eyestate_sklearn_NOPASS.ipynb
- 26. H2O.ai Machine Intelligence H2O on https://www.kaggle.com/mlandry •H2O starter scripts available on Kaggle • H2O is used in many competitions on Kaggle • Mark Landry, H2O Data Scientist and Competitive Kaggler
- 27. H2O.ai Machine Intelligence Where tolearn more? • H2O Online Training (free): http://learn.h2o.ai • H2O Slidedecks: http://www.slideshare.net/0xdata • H2O Video Presentations: https://www.youtube.com/user/0xdata • H2O Community Events & Meetups: http://h2o.ai/events • Machine Learning & Data Science courses: http://coursebuffet.com
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- 29. H2O.ai Machine Intelligence Thank you! @ledellon Twitter, GitHub erin@h2o.ai http://www.stat.berkeley.edu/~ledell