Machine Learning for a Rescue

Machine Learning for a Rescue

code::dive conference via YouTube Direct link

1936, RONALD FISHER IRIS DATASET

16 of 24

16 of 24

1936, RONALD FISHER IRIS DATASET

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Machine Learning for a Rescue

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  1. 1 Intro
  2. 2 RESCUE
  3. 3 Source Ministry
  4. 4 CLIENT PROBLEM
  5. 5 M BACKLINKS CLASSIFY THEM
  6. 6 1ST APPROACH IF-OLOGY UGLY CODE FOR POC
  7. 7 ND APPROACH NAIVE MACHINE LEARNING
  8. 8 DOING WITHOUT KNOWING ISA. RECIPE FOR A FAILURE
  9. 9 RD APPROACH, FINAL DATA ORIENTED MACHINE LEARNING WORKFLOW
  10. 10 CLASSIFICATION REGRESSION CLUSTERING DIMENSIONALITY REDUCTION ASSOCIATION RULES
  11. 11 SUPERVISED LEARNING UNSUPERVISED LEARNING REINFORCEMENT LEARNING
  12. 12 OUR PROBLEM
  13. 13 DEVELOPERS DATASET
  14. 14 REGRESSION PREDICTING VALUES
  15. 15 CLUSTERING K-MEANS
  16. 16 1936, RONALD FISHER IRIS DATASET
  17. 17 RESULTS STABILITY
  18. 18 CLASSIFICATION FAST ARTIFICIAL NEURAL NETWORK
  19. 19 HOW TO CLASSIFY OUR DATASET AUTOMATED WAY TO FIND JUNIOR/SENIOR DEVELOPER?
  20. 20 TECHNOLOGY
  21. 21 FOCUS ON IDEAS NOT TOOLS
  22. 22 ML IS NOT A SINGLE RUN
  23. 23 IT'S A PROCESS
  24. 24 DEFINE A PROBLEM ANALYZE YOUR DATA UNDERSTAND YOUR DATA PREPARE DATA FOR ML SELECT & RUN ALGO(S) TUNE ALGO(S) PARAMETERS SELECT FINAL MODEL VALIDATE FINAL MODEL

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