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

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文档大小：172.0 MB
文档个数：64个文档
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收录时间：2020-02-05
最近下载：2025-02-13
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文档列表

29 - What is model monitoring.mp4 11.7 MB
26 - Batch vs. real-time scoring.mp4 10.0 MB
30 - How often should you rebuild.mp4 9.4 MB
10 - Assessing data.mp4 9.3 MB
22 - Modeling with missing data.mp4 8.7 MB
24 - Scoring a black box model.mp4 8.4 MB
13 - Data and the data scientist.mp4 8.3 MB
12 - Seasonality and time alignment.mp4 7.3 MB
05 - The stages of predictive analytics data.mp4 6.9 MB
09 - Who truly has big data.mp4 6.7 MB
01 - Scaling machine learning initiatives.mp4 6.5 MB
04 - The nine big data bottlenecks.mp4 6.1 MB
23 - Scoring traditional ML models.mp4 5.1 MB
27 - Data prep and scoring.mp4 5.0 MB
17 - Understanding the modeling process.mp4 4.9 MB
25 - Scoring an ensemble.mp4 4.7 MB
16 - Feature engineering.mp4 4.4 MB
21 - How to sample properly.mp4 4.4 MB
28 - Combining batch and real-time scoring.mp4 4.0 MB
07 - How much data do I need.mp4 3.9 MB
03 - Data and supervised machine learning.mp4 3.9 MB
20 - Slow algorithms More models.mp4 3.8 MB
11 - Selecting Data that should be left out.mp4 3.7 MB
02 - Defining terms.mp4 3.6 MB
14 - Aggregate and restructure.mp4 3.6 MB
08 - Balancing.mp4 3.4 MB
15 - Dummy coding.mp4 3.2 MB
19 - Slow algorithms More calculations.mp4 3.2 MB
18 - Slow algorithms Brute force.mp4 3.0 MB
06 - Why you might have too little data.mp4 2.9 MB
31 - Next steps.mp4 1.8 MB
26 - Batch vs. real-time scoring.en.srt 8.0 kB
29 - What is model monitoring.en.srt 7.5 kB
12 - Seasonality and time alignment.en.srt 7.2 kB
30 - How often should you rebuild.en.srt 7.2 kB
04 - The nine big data bottlenecks.en.srt 6.6 kB
22 - Modeling with missing data.en.srt 6.1 kB
09 - Who truly has big data.en.srt 6.1 kB
10 - Assessing data.en.srt 6.1 kB
27 - Data prep and scoring.en.srt 5.5 kB
17 - Understanding the modeling process.en.srt 5.4 kB
24 - Scoring a black box model.en.srt 5.2 kB
05 - The stages of predictive analytics data.en.srt 5.2 kB
23 - Scoring traditional ML models.en.srt 5.2 kB
16 - Feature engineering.en.srt 5.1 kB
13 - Data and the data scientist.en.srt 4.8 kB
21 - How to sample properly.en.srt 4.4 kB
20 - Slow algorithms More models.en.srt 4.1 kB
07 - How much data do I need.en.srt 3.8 kB
15 - Dummy coding.en.srt 3.8 kB
03 - Data and supervised machine learning.en.srt 3.7 kB
11 - Selecting Data that should be left out.en.srt 3.7 kB
14 - Aggregate and restructure.en.srt 3.5 kB
08 - Balancing.en.srt 3.4 kB
18 - Slow algorithms Brute force.en.srt 3.2 kB
02 - Defining terms.en.srt 3.2 kB
25 - Scoring an ensemble.en.srt 3.2 kB
28 - Combining batch and real-time scoring.en.srt 3.0 kB
06 - Why you might have too little data.en.srt 3.0 kB
01 - Scaling machine learning initiatives.en.srt 3.0 kB
19 - Slow algorithms More calculations.en.srt 2.9 kB
31 - Next steps.en.srt 1.6 kB
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