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TORRENT DETAILS
[UdemyCourseDownloader] Machine Learning And AI Foundations Predictive Modeling Strategy At Scale
TORRENT SUMMARY
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Building world-class predictive analytics solutions requires recognizing that the challenges of scale and sample size fluctuate greatly at different stages of a project. How do you know how much data to use? What is too little, what is too much? How does your infrastructure need to scale with the volume and demands of the project? This course walks step by step through the strategic and tactical aspects of determining how much data is needed to build an effective predictive modeling solution based on machine learning and what volumes of data are so large that they will create challenges. Instructor Keith McCormick reviews each stage—data selection, data preparation, modeling, scoring, and deployment—with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools.
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FILE LIST
Filename
Size
01 - Scaling machine learning initiatives.en.srt
2.9 KB
01 - Scaling machine learning initiatives.mp4
6.2 MB
02 - Defining terms.en.srt
3.1 KB
02 - Defining terms.mp4
3.5 MB
03 - Data and supervised machine learning.en.srt
3.6 KB
03 - Data and supervised machine learning.mp4
3.7 MB
04 - The nine big data bottlenecks.en.srt
6.4 KB
04 - The nine big data bottlenecks.mp4
5.9 MB
05 - The stages of predictive analytics data.en.srt
5.1 KB
05 - The stages of predictive analytics data.mp4
6.6 MB
06 - Why you might have too little data.en.srt
2.9 KB
06 - Why you might have too little data.mp4
2.8 MB
07 - How much data do I need.en.srt
3.7 KB
07 - How much data do I need.mp4
3.8 MB
08 - Balancing.en.srt
3.3 KB
08 - Balancing.mp4
3.2 MB
09 - Who truly has big data.en.srt
5.9 KB
09 - Who truly has big data.mp4
6.4 MB
10 - Assessing data.en.srt
5.9 KB
10 - Assessing data.mp4
8.9 MB
11 - Selecting Data that should be left out.en.srt