UdemyCourseDownloader Machine Learning and AI Foundations Predictive Modeling Strategy at Scale

1. 29 - What is model monitoring.mp411.12MB
2. 01 - Scaling machine learning initiatives.mp46.18MB
3. 02 - Defining terms.mp43.46MB
4. 03 - Data and supervised machine learning.mp43.72MB
5. 04 - The nine big data bottlenecks.mp45.86MB
6. 05 - The stages of predictive analytics data.mp46.6MB
7. 06 - Why you might have too little data.mp42.78MB
8. 07 - How much data do I need.mp43.76MB
9. 08 - Balancing.mp43.22MB
10. 09 - Who truly has big data.mp46.38MB
11. 10 - Assessing data.mp48.89MB
12. 11 - Selecting Data that should be left out.mp43.49MB
13. 12 - Seasonality and time alignment.mp46.93MB
14. 13 - Data and the data scientist.mp47.92MB
15. 14 - Aggregate and restructure.mp43.42MB
16. 15 - Dummy coding.mp43.1MB
17. 16 - Feature engineering.mp44.21MB
18. 17 - Understanding the modeling process.mp44.68MB
19. 18 - Slow algorithms Brute force.mp42.91MB
20. 19 - Slow algorithms More calculations.mp43.03MB
21. 20 - Slow algorithms More models.mp43.65MB
22. 21 - How to sample properly.mp44.19MB
23. 22 - Modeling with missing data.mp48.33MB
24. 23 - Scoring traditional ML models.mp44.84MB
25. 24 - Scoring a black box model.mp47.99MB
26. 25 - Scoring an ensemble.mp44.44MB
27. 26 - Batch vs. real-time scoring.mp49.51MB
28. 27 - Data prep and scoring.mp44.74MB
29. 28 - Combining batch and real-time scoring.mp43.84MB
30. 30 - How often should you rebuild.mp48.95MB
31. 31 - Next steps.mp41.71MB