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Intro to scikit-learn

In this talk by AWeber's Michael Becker, you will get a brief overview of Machine Learning and scikit-learn. This is a scaled down version of this talk from Pycon 2013: http://github.com/jakevdp/sklearn_pycon2013

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Intro to scikit-learn

  1. 1. Intro to scikit-learn Michael Becker PyData Boston 2013
  2. 2. Who is this guy? Software Engineer @ AWeber Founder of the DataPhilly Meetup group @beckerfuffle beckerfuffle.com These slides and more @ github.com/mdbecker
  3. 3. On the shoulders of giants • Machine Learning 101 tutorial from scikit-learn.
  4. 4. On the shoulders of giants • Machine Learning 101 tutorial from scikit-learn. • IPython notebooks from pycon 2013.
  5. 5. What is Machine Learning?
  6. 6. What is Machine Learning?
  7. 7. Data in scikit-learn • Stored as a 2d-array • [n_samples, n_features] • n_samples: items to process • n_features: distinct traits
  8. 8. The Iris Dataset
  9. 9. The Iris Dataset
  10. 10. The Iris Dataset
  11. 11. The Iris Dataset: Loading
  12. 12. The Iris Dataset: Loading
  13. 13. The Iris Dataset: Loading
  14. 14. The Iris Dataset: Loading
  15. 15. The Iris Dataset: Loading
  16. 16. The Iris Dataset: Loading
  17. 17. The Iris Dataset: Loading
  18. 18. Machine Learning: Supervised
  19. 19. Machine Learning: Unsupervised
  20. 20. Scikit-learn's interface
  21. 21. Scikit-learn's interface
  22. 22. Feature Extraction Often data is unstructured & non-numerical: •Text documents
  23. 23. Feature Extraction Often data is unstructured & non-numerical: •Text documents •Images
  24. 24. Feature Extraction Often data is unstructured & non-numerical: •Text documents •Images •Sounds
  25. 25. Supervised Learning: Classification
  26. 26. Supervised Learning: Classification
  27. 27. Supervised Learning: Classification
  28. 28. Supervised Learning: Classification
  29. 29. Supervised Learning: Classification
  30. 30. Supervised Learning: Classification
  31. 31. Supervised Learning: Classification
  32. 32. Supervised Learning: Classification
  33. 33. Supervised Learning: Classification
  34. 34. Supervised Learning: Classification • Email classification • Language identification • New article categorization • Sentiment analysis • Facial recognition • ...
  35. 35. Unsupervised Learning
  36. 36. Dimensionality Reduction
  37. 37. Principal Component Analysis
  38. 38. Unsupervised Learning: PCA
  39. 39. Unsupervised Learning: PCA
  40. 40. Unsupervised Learning: PCA
  41. 41. Unsupervised Learning: PCA
  42. 42. Validation & Testing
  43. 43. Validation & Testing
  44. 44. Validation & Testing
  45. 45. Overfitting
  46. 46. Cross-Validation
  47. 47. Cross-Validation
  48. 48. Cross-Validation
  49. 49. Additional Resources • Machine Learning 101 tutorial from scikit-learn.
  50. 50. Additional Resources • IPython notebooks from pycon 2013.
  51. 51. My info Tweet me @beckerfuffle Find me at beckerfuffle.com These slides and more @ github.com/mdbecker

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