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Think Machine Learning with Scikit-Learn (Python)


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The Data Science Lab, KSKV Kachchh University Presents Think Machine Learning Talk given by Chetan Khatri

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Think Machine Learning with Scikit-Learn (Python)

  1. 1. Think Machine Learning with Scikit- learn (Python) By: Chetan Khatri Principal Big Data Engineer, Nazara Technologies. Data Science Lab, The Department of Computer Science, University of Kachchh.
  2. 2. About me l- Principal Big Data Engineer, Nazara Technologies. l- Technical Reviewer – Packt Publication. l- Ex. Developer - Eccella Corporation. lAlumni, The Department of Computer Science, KSKV Kachchh University.
  3. 3. Outline lAn Introduction to Machine Learning lHello World in Machine learning with 6 lines of code lVisualizing a Decision Tree lClassifying Images lSupervised learning : Pipeline lWriting first Classifier
  4. 4. Early Days AI Programs : Deep Blue
  5. 5. Now, AI Programs lAlpha go is best example, wrote for Playing Go game, but it can play Atari games also.
  6. 6. Machine Learning lMachine Learning does this possible, it is study of algorithms which learns from examples and experience having set of rules and hard coded lines. l“Learns from Examples and Experience”
  7. 7. Let's have problem lLet's have problem: It seems easy but difficult to solve without machine learning.
  8. 8. Open Source Libraries
  9. 9. Classifier
  10. 10. Scikit-learn
  11. 11. Test ! No error ! Yay !!
  12. 12. Supervised Learning Collecting Training Data Train Classifier Make Predictions
  13. 13. Training Data Weight Texture Label 150g Bumpy Orange 170g Bumpy Orange 140g Smooth Apple 130g Smooth Apple Feature s Example s
  14. 14. Training Data
  15. 15. Important Concepts lHow does this work in Real world ? lHow much training data do you need ? lHow is the tree created ? lWhat makes a good feature ?
  16. 16. Many Types of Classifier lArtificial Neural Network (ANN) lSupport Vector Machine (SVM) lNearest Neighbour classifier (KNN) lRandom Forest (RF) lGradient Boosting Machine (GBM) lEtc.. lEtc..
  17. 17. Demo
  18. 18. 2. Visualizing a Decision Tree
  19. 19. 3. What Makes a Good Feature? lImagine we want to write classifier to classify two types of dogs.
  20. 20. Variation in the world !
  21. 21. lHands - On
  22. 22. About 80% of dogs at this height are labs
  23. 23. About 95% of dogs at this height are greyhounds
  24. 24. lFeature captures different types of information
  25. 25. Thought Experiment
  26. 26. Avoid useless features
  27. 27. Independent features are best
  28. 28. lHeight in Inches lHeight in centimeters
  29. 29. lHeight in Inches lHeight in centimeters
  30. 30. lAvoid Redundant features lFeature should be easy to understand
  31. 31. lSimpler relationships are easier to learn. lIdeal features are: lInformative lIndependent lSimple
  32. 32. 4. Pipeline - Machine Learning
  33. 33. l
  34. 34. 5. Writing our first classifier
  35. 35. lMeasure Distance
  36. 36. Demo lImplement nearest neighbor Algorithm
  37. 37. Next Step
  38. 38. Thank you