[UdemyCourseDownloader] Machine Learning and AI Foundations Predictive Modeling Strategy at Scale
File List
- 29 - What is model monitoring.mp4 11.1 MB
- 26 - Batch vs. real-time scoring.mp4 9.5 MB
- 30 - How often should you rebuild.mp4 8.9 MB
- 10 - Assessing data.mp4 8.9 MB
- 22 - Modeling with missing data.mp4 8.3 MB
- 24 - Scoring a black box model.mp4 8.0 MB
- 13 - Data and the data scientist.mp4 7.9 MB
- 12 - Seasonality and time alignment.mp4 6.9 MB
- 05 - The stages of predictive analytics data.mp4 6.6 MB
- 09 - Who truly has big data.mp4 6.4 MB
- 01 - Scaling machine learning initiatives.mp4 6.2 MB
- 04 - The nine big data bottlenecks.mp4 5.9 MB
- 23 - Scoring traditional ML models.mp4 4.8 MB
- 27 - Data prep and scoring.mp4 4.7 MB
- 17 - Understanding the modeling process.mp4 4.7 MB
- 25 - Scoring an ensemble.mp4 4.4 MB
- 16 - Feature engineering.mp4 4.2 MB
- 21 - How to sample properly.mp4 4.2 MB
- 28 - Combining batch and real-time scoring.mp4 3.8 MB
- 07 - How much data do I need.mp4 3.8 MB
- 03 - Data and supervised machine learning.mp4 3.7 MB
- 20 - Slow algorithms More models.mp4 3.7 MB
- 11 - Selecting Data that should be left out.mp4 3.5 MB
- 02 - Defining terms.mp4 3.5 MB
- 14 - Aggregate and restructure.mp4 3.4 MB
- 08 - Balancing.mp4 3.2 MB
- 15 - Dummy coding.mp4 3.1 MB
- 19 - Slow algorithms More calculations.mp4 3.0 MB
- 18 - Slow algorithms Brute force.mp4 2.9 MB
- 06 - Why you might have too little data.mp4 2.8 MB
- 31 - Next steps.mp4 1.7 MB
- 26 - Batch vs. real-time scoring.en.srt 7.9 KB
- 29 - What is model monitoring.en.srt 7.4 KB
- 12 - Seasonality and time alignment.en.srt 7.0 KB
- 30 - How often should you rebuild.en.srt 7.0 KB
- 04 - The nine big data bottlenecks.en.srt 6.4 KB
- 22 - Modeling with missing data.en.srt 6.0 KB
- 09 - Who truly has big data.en.srt 5.9 KB
- 10 - Assessing data.en.srt 5.9 KB
- 27 - Data prep and scoring.en.srt 5.4 KB
- 17 - Understanding the modeling process.en.srt 5.3 KB
- 24 - Scoring a black box model.en.srt 5.1 KB
- 05 - The stages of predictive analytics data.en.srt 5.1 KB
- 23 - Scoring traditional ML models.en.srt 5.0 KB
- 16 - Feature engineering.en.srt 5.0 KB
- 13 - Data and the data scientist.en.srt 4.7 KB
- 21 - How to sample properly.en.srt 4.3 KB
- 20 - Slow algorithms More models.en.srt 4.0 KB
- 07 - How much data do I need.en.srt 3.7 KB
- 15 - Dummy coding.en.srt 3.7 KB
- 03 - Data and supervised machine learning.en.srt 3.6 KB
- 11 - Selecting Data that should be left out.en.srt 3.6 KB
- 14 - Aggregate and restructure.en.srt 3.4 KB
- 08 - Balancing.en.srt 3.3 KB
- 18 - Slow algorithms Brute force.en.srt 3.2 KB
- 02 - Defining terms.en.srt 3.1 KB
- 25 - Scoring an ensemble.en.srt 3.1 KB
- 28 - Combining batch and real-time scoring.en.srt 2.9 KB
- 06 - Why you might have too little data.en.srt 2.9 KB
- 01 - Scaling machine learning initiatives.en.srt 2.9 KB
- 19 - Slow algorithms More calculations.en.srt 2.8 KB
- 31 - Next steps.en.srt 1.6 KB
- udemycoursedownloader.com.url 132 bytes
- Udemy Course downloader.txt 94 bytes
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