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Machine Learning 101 for Product Managers by Amazon Sr PM

Main takeaways:
- How ML works and when to use it
- How to work with data science teams to launch products
- Real world applications

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Machine Learning 101 for Product Managers by Amazon Sr PM

  1. 1. www.productschool.com Machine Learning 101 for Product Managers by Amazon Sr PM
  2. 2. CERTIFICATES Your Product Management Certificate Path Product Leadership Certificate™ Full Stack Product Management Certificate™ Product Management Certificate™ 20 HOURS 40 HOURS 40 HOURS
  3. 3. Corporate Training Level up your team’s Product Management skills
  4. 4. Free Product Management Resources BOOKS EVENTS JOB PORTAL COMMUNITIES bit.ly/product_resources COURSES
  5. 5. Machine Learning 101 For Product Managers Carl Betzler Sr. Product Manager – Amazon www.carlbetzler.com
  6. 6. Disclaimer: opinions expressed are solely my own and do not express the views or opinions of my employer.
  7. 7. What is Machine Learning (ML)? Field of study that gives computers the ability to learn without being explicitly programmed -Arthur Samuel, 1959
  8. 8. Classical Programming Data Rules Answers Machine Learning Data Rules Answers
  9. 9. Artificial Intelligence Machine Learning Deep Learning
  10. 10. Why is ML important? https://www.forbes.com/sites/louiscolumbus/2018/01/12/10-charts-that-will-cha nge-your-perspective-on-artificial-intelligences-growth/?sh=2bd87c194758
  11. 11. How did we get here? 2016: Google’s AI beats humans in Go 1763: foundations of Bayes’ Theorem are published 1950: Alan Turing proposes a “learning machine” 1956: the term “Artificial Intelligence” is coined by John McCarthy 1967: the nearest neighbor algorithm is created 1986: backprop is applied to neural networks 1995: first work on support vector machines is published
  12. 12. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  13. 13. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  14. 14. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  15. 15. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  16. 16. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  17. 17. What problems can ML solve? Ranking Recommendation Classification Regression Clustering Anomaly Detection
  18. 18. Types of ML Supervised Learning Unsupervised Learning Big Difference: LABELS
  19. 19. Supervised Learning Supervised Learning Regression Classification
  20. 20. Supervised Learning Supervised Learning Regression Classification Predicts a continuous, numeric outcome variable (y) based on the value of one or multiple predictor variables (x) Definition:
  21. 21. Supervised Learning Graphical Representation: Supervised Learning Regression Classification
  22. 22. Supervised Learning Supervised Learning Regression Classification Where a class label is predicted for a given example of input data Definition:
  23. 23. Supervised Learning Graphical Representation: Supervised Learning Regression Classification
  24. 24. Supervised Learning - Metrics ● Root Mean Squared Error ● R-squared Regression Classification ● Accuracy ● Precision ● Recall
  25. 25. Supervised Learning - Metrics Classification True Positive 10 False Negative 8 True Negative 2 False Positive 1 True State Prediction Positive Negative Positive Negative
  26. 26. Supervised Learning - Metrics Classification True Positive 10 False Negative 8 True Negative 2 False Positive 1 True State Prediction Positive Negative Positive Negative CorrectTrue Predictions Precision: AllTrue Predictions
  27. 27. Supervised Learning - Metrics Classification True Positive 10 False Negative 8 True Negative 2 False Positive 1 True State Prediction Positive Negative Positive Negative CorrectTrue Predictions Recall: AllTrue Cases
  28. 28. Other Metrics ● Logarithmic Loss ● Area Under ROC Curve ● Mean Absolute Error ● … and many others! Not sure what to track? Ask an expert!
  29. 29. When do I use ML? Complexity Scalability Personaliz-ati on Adaptability Available Secure Relevant Unbiased Does your problem require… Is your data…
  30. 30. How do I launch an ML product? The same way you launch any other product! Who is the Customer? What is the problem? What is the benefit? How do you know what Customers want? Do you have data to support your idea? What does the CX look like? Consider:
  31. 31. How do I launch an ML product? Is it possible to build an MLP without ML? Build the MLP and add incremental value with ML Yes No Engage scientists and tech teams ASAP
  32. 32. Dos and Don’ts Do… ● Experiment! ● Think backwards from the Customer ● Ask for advice from scientists and ML experts ● Engage your tech teams early and often Don’t… ● Use ML without an appropriate reason ● Forget to solve the problem ● Launch with “bad” data
  33. 33. Thank you and have fun!
  34. 34. www.productschool.com Part-time Product Management Training Courses and Corporate Training

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