[UdemyCourseDownloader] Machine Learning and AI Foundations Predictive Modeling Strategy at Scale
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2018-12-31 10:09 | 2024-9-29 05:05 | 192 | 163.85 MB | 31 |
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文件列表
- 29 - What is model monitoring.mp411.12MB
- 01 - Scaling machine learning initiatives.mp46.18MB
- 02 - Defining terms.mp43.46MB
- 03 - Data and supervised machine learning.mp43.72MB
- 04 - The nine big data bottlenecks.mp45.86MB
- 05 - The stages of predictive analytics data.mp46.6MB
- 06 - Why you might have too little data.mp42.78MB
- 07 - How much data do I need.mp43.76MB
- 08 - Balancing.mp43.22MB
- 09 - Who truly has big data.mp46.38MB
- 10 - Assessing data.mp48.89MB
- 11 - Selecting Data that should be left out.mp43.49MB
- 12 - Seasonality and time alignment.mp46.93MB
- 13 - Data and the data scientist.mp47.92MB
- 14 - Aggregate and restructure.mp43.42MB
- 15 - Dummy coding.mp43.1MB
- 16 - Feature engineering.mp44.21MB
- 17 - Understanding the modeling process.mp44.68MB
- 18 - Slow algorithms Brute force.mp42.91MB
- 19 - Slow algorithms More calculations.mp43.03MB
- 20 - Slow algorithms More models.mp43.65MB
- 21 - How to sample properly.mp44.19MB
- 22 - Modeling with missing data.mp48.33MB
- 23 - Scoring traditional ML models.mp44.84MB
- 24 - Scoring a black box model.mp47.99MB
- 25 - Scoring an ensemble.mp44.44MB
- 26 - Batch vs. real-time scoring.mp49.51MB
- 27 - Data prep and scoring.mp44.74MB
- 28 - Combining batch and real-time scoring.mp43.84MB
- 30 - How often should you rebuild.mp48.95MB
- 31 - Next steps.mp41.71MB
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