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 Evaluating Classification and Regression Systems (Machine Learning with Python for Everyone Series), Part 2

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Evaluating Classification and Regression Systems  (Machine Learning with Python for Everyone Series), Part 2 Empty
MensajeTema: Evaluating Classification and Regression Systems (Machine Learning with Python for Everyone Series), Part 2   Evaluating Classification and Regression Systems  (Machine Learning with Python for Everyone Series), Part 2 EmptySáb Abr 24, 2021 4:36 am

Evaluating Classification and Regression Systems  (Machine Learning with Python for Everyone Series), Part 2 F82ed09d157a9d745d4d479b6a5b019a
h264, yuv420p, 1280x720 | ENGLISH, 48000 Hz, 2channels | 3h 45mn | 6.82 GB

4 Hours of Video Instruction

vDescription

Code-along sessions move you from introductory machine learning concepts to concrete code.

Overview

Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.

You learn about the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You will learn techniques for getting the most informative learning performance measures out of your data. You will come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.

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Evaluating Classification and Regression Systems  (Machine Learning with Python for Everyone Series), Part 2 E51dcf41889653b3d09c3157b375da9a

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https://rapidgator.net/file/e4db85c0876878ef758fb09999c3a815/te714.Evaluating.Classification.and.Regression.Systems.Machine.Learning.with.Python.for.Everyone.Series.Part.2.part1.rar.html
https://rapidgator.net/file/5c60759634740f5053bb1b13ab6b494b/te714.Evaluating.Classification.and.Regression.Systems.Machine.Learning.with.Python.for.Everyone.Series.Part.2.part2.rar.html
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