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|a 9780136932604
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|a Fenner, Mark
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|a Evaluating Classification and Regression Systems (Machine Learning with Python for Everyone Series), Part 2
|c Fenner, Mark
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| 250 |
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|a 1st edition
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| 260 |
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|b Addison-Wesley Professional
|c 2021
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| 300 |
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|a 1 video file, approximately 3 hr., 45 min.
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|a Streaming video / http://id.loc.gov/authorities/subjects/sh2005005237
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|a streaming video / aat
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|a Internet videos / http://id.loc.gov/authorities/subjects/sh2007001612
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|a Vidéo en continu
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|a Vidéos sur Internet
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Made available through: Safari, an O'Reilly Media Company
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|u https://learning.oreilly.com/videos/~/9780136932604/?ar
|x Verlag
|3 Volltext
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|a 000
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|a Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. 4 Hours of Video Instruction Description 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.
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|a 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. About the Instructor Mark Fenner, PhD, has been teaching computing and mathematics to diverse adult audiences since 1999. His research projects have addressed design, implementation, and performance of machine learning and numerical algorithms, learning systems for security analysis of software repositories and intrusion detection, probabilistic models of protein function, and analysis and visualization of ecological and microscopy data. Mark continues to work across the data science spectrum from C, Fortran, and Python implementation to statistical analysis and visualization. He has delivered training and developed curriculum for Fortune 50 companies, boutique consultancies, and national-level research laboratories. Mark holds a Ph. D.
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|a in Computer Science and owns Fenner Training and Consulting, LLC. Skill Level Beginner to Intermediate Learn How To Recognize underfitting and overfitting with graphical plots. Make use of resampling techniques like cross-validation to get the most out of your data. Graphically evaluate the learning performance of learning systems Compare production learners with baseline models over various classification metrics Build and evaluate confusion matrices and ROC curves Apply classification metrics to multi-class learning problems Develop precision-recall and lift curves for cla ...
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