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[UdemyCourseDownloader] Machine Learning and AI Foundations Predictive Modeling Strategy at Scale

  • 收录时间:2020-01-31 18:42:42
  • 文件大小:164MB
  • 下载次数:31
  • 最近下载:2021-01-08 13:17:59
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文件列表

  1. 29 - What is model monitoring.mp4 11MB
  2. 26 - Batch vs. real-time scoring.mp4 10MB
  3. 30 - How often should you rebuild.mp4 9MB
  4. 10 - Assessing data.mp4 9MB
  5. 22 - Modeling with missing data.mp4 8MB
  6. 24 - Scoring a black box model.mp4 8MB
  7. 13 - Data and the data scientist.mp4 8MB
  8. 12 - Seasonality and time alignment.mp4 7MB
  9. 05 - The stages of predictive analytics data.mp4 7MB
  10. 09 - Who truly has big data.mp4 6MB
  11. 01 - Scaling machine learning initiatives.mp4 6MB
  12. 04 - The nine big data bottlenecks.mp4 6MB
  13. 23 - Scoring traditional ML models.mp4 5MB
  14. 27 - Data prep and scoring.mp4 5MB
  15. 17 - Understanding the modeling process.mp4 5MB
  16. 25 - Scoring an ensemble.mp4 4MB
  17. 16 - Feature engineering.mp4 4MB
  18. 21 - How to sample properly.mp4 4MB
  19. 28 - Combining batch and real-time scoring.mp4 4MB
  20. 07 - How much data do I need.mp4 4MB
  21. 03 - Data and supervised machine learning.mp4 4MB
  22. 20 - Slow algorithms More models.mp4 4MB
  23. 11 - Selecting Data that should be left out.mp4 3MB
  24. 02 - Defining terms.mp4 3MB
  25. 14 - Aggregate and restructure.mp4 3MB
  26. 08 - Balancing.mp4 3MB
  27. 15 - Dummy coding.mp4 3MB
  28. 19 - Slow algorithms More calculations.mp4 3MB
  29. 18 - Slow algorithms Brute force.mp4 3MB
  30. 06 - Why you might have too little data.mp4 3MB
  31. 31 - Next steps.mp4 2MB
  32. 26 - Batch vs. real-time scoring.en.srt 8KB
  33. 29 - What is model monitoring.en.srt 7KB
  34. 12 - Seasonality and time alignment.en.srt 7KB
  35. 30 - How often should you rebuild.en.srt 7KB
  36. 04 - The nine big data bottlenecks.en.srt 6KB
  37. 22 - Modeling with missing data.en.srt 6KB
  38. 09 - Who truly has big data.en.srt 6KB
  39. 10 - Assessing data.en.srt 6KB
  40. 27 - Data prep and scoring.en.srt 5KB
  41. 17 - Understanding the modeling process.en.srt 5KB
  42. 24 - Scoring a black box model.en.srt 5KB
  43. 05 - The stages of predictive analytics data.en.srt 5KB
  44. 23 - Scoring traditional ML models.en.srt 5KB
  45. 16 - Feature engineering.en.srt 5KB
  46. 13 - Data and the data scientist.en.srt 5KB
  47. 21 - How to sample properly.en.srt 4KB
  48. 20 - Slow algorithms More models.en.srt 4KB
  49. 07 - How much data do I need.en.srt 4KB
  50. 15 - Dummy coding.en.srt 4KB
  51. 03 - Data and supervised machine learning.en.srt 4KB
  52. 11 - Selecting Data that should be left out.en.srt 4KB
  53. 14 - Aggregate and restructure.en.srt 3KB
  54. 08 - Balancing.en.srt 3KB
  55. 18 - Slow algorithms Brute force.en.srt 3KB
  56. 02 - Defining terms.en.srt 3KB
  57. 25 - Scoring an ensemble.en.srt 3KB
  58. 28 - Combining batch and real-time scoring.en.srt 3KB
  59. 06 - Why you might have too little data.en.srt 3KB
  60. 01 - Scaling machine learning initiatives.en.srt 3KB
  61. 19 - Slow algorithms More calculations.en.srt 3KB
  62. 31 - Next steps.en.srt 2KB
  63. udemycoursedownloader.com.url 132B
  64. Udemy Course downloader.txt 94B