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That Conference 2017 - Building Real-World Solutions with Machine Learning

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Nexosis CTO / Co-Founder Jason Montgomery presented this deck at That Conference 2017 on August 8th, 2017.

https://www.thatconference.com/Sessions/Session/11775

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That Conference 2017 - Building Real-World Solutions with Machine Learning

  1. 1. 1 Machine Learning for Developers Building Real-World Solutions with Machine Learning Jason Montgomery, CTO / Co-Founder Nexosis 08/08/2017
  2. 2. 2 Hype or Not? What is machine learning?
  3. 3. 3 A Brief History
  4. 4. 4 Hype or Not? Security + Video Games = Can we detect online cheating using player stats?
  5. 5. 5 R&D Phase – Learn, See, Do! o https://www.coursera.org/learn/machine-learning/
  6. 6. 6 We Made This
  7. 7. 7 We Made This Prep Data- Reduce, Filter, Scaling, Imputation, Project etc… Algorithm Builds Model Using Data Prep Data- Reduce, Filter, Scaling, Imputation, Project etc… Machine Learning Return Prediction Data To Classify Enters
  8. 8. 8 The Computer Built This
  9. 9. 9 Results
  10. 10. 10 What is Machine Learning? Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.” Wikipedia https://en.wikipedia.org/wiki/Machine_learning
  11. 11. 11 What is Machine Learning? “…machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.” Wikipedia https://en.wikipedia.org/wiki/Machine_learning
  12. 12. 12 What is Machine Learning?
  13. 13. 13 What is Machine Learning? “Machine Learning” is a great marketing word for what ten years ago would have just been called “Math” -@dildog Christian Rioux https://twitter.com/dildog/status/457674859573956608
  14. 14. 14 Learn ML - The Hard Way o Learn Multivariate Calculus o Learn Linear Algebra o Probability Theory & Statistical Inference o Learn Algorithm Design and Analysis o Learn to Code (probably python or maybe R) o Take Machine Learning Courses o scikit-learn / Kaggle Competitions Source https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/Course
  15. 15. 15 o Learn Multivariate Calculus o Learn Linear Algebra o Probability Theory & Statistical Inference o Learn Algorithm Design and Analysis o Learn to Code (probably python or maybe R) o Take Machine Learning Courses o scikit-learn / Kaggle Competitions Source https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/Course Learn ML - The Hard Way
  16. 16. 16 o Learn Multivariate Calculus o Learn Linear Algebra o Probability Theory & Statistical Inference o Learn Algorithm Design and Analysis o Learn to Code (probably python or maybe R) o Take Machine Learning Courses o scikit-learn / Kaggle Competitions Source https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/Course Learn ML - The Hard Way
  17. 17. 17 Sounds Neat, But I Don’t Aspire To That…
  18. 18. 18 ML Can Help Demand Forecasting Regression Analysis Classification Anomaly Detection Impact Analysis / What-If?
  19. 19. 19 ML Can Help Demand Forecasting Regression Analysis Classification Anomaly Detection Impact Analysis / What-If?
  20. 20. 20 All Industries / Verticals Use Cases
  21. 21. 21 Machine Learning for Developers
  22. 22. 22 Machine Learning for Developers
  23. 23. 23 Machine Learning for Developers
  24. 24. 24 Machine Learning for Developers
  25. 25. 25 Machine Learning for Developers
  26. 26. 26 Machine Learning for Developers
  27. 27. 27 Machine Learning for Developers
  28. 28. 28 Machine Learning for Developers
  29. 29. 29 Is it that easy? Sometimes, but not always….
  30. 30. 30 You Have To Know Your Data!
  31. 31. 31 You Have To Know Your Data!
  32. 32. 32 What If My Data Is Crap? Garbage in, Garbage out.
  33. 33. 33 Sales Forecasting http://docs.nexosis.com/tutorials/salesforecasting
  34. 34. 34 Sales on Event Day – Causal Impact Analysis
  35. 35. 35 Impact Air Quality – Causal Impact Analysis http://docs.nexosis.com/tutorials/beijingpm25
  36. 36. 36 Dungeon Crawl Stone Soup – Causal Impact Analysis http://docs.nexosis.com/tutorials/dungeoncrawlimpact
  37. 37. 37 House Values – Time-Series Forecasting w/ Zillow Data http://docs.nexosis.com/tutorials/forecasthousevalue
  38. 38. 38 Energy o Predict Energy Demand o Determine the Impact of weather on demand o Determine the impact of a storm on revenue o Predict Electricity Prices o Detect Energy Fraud o IIoT / IoT o Predict Wind/Solar Generation based on Weather o Equipment maintenance needs o Manufacturing o Forecast demand to determine proper manufacturing levels o Determine impact of downtime on manufacturing levels Customer Service o Predict Call Volume o Call volume impact of new release o Predict Staffing Levels o Plan Infrastructure Needs o Predict equipment usage o Predict Wait Times o Distribution and Logistics o Impact of disruptions on Supply Chain o Forecast Demand of Products o Predict Raw Goods Needed o Operations o Determine impact of weather or illness on overall productivity More Examples
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