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Synopsis
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. Youâ ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.
This pocket reference includes sections that cover:
- Classification, using the Titanic dataset
- Cleaning data and dealing with missing data
- Exploratory data analysis
- Common preprocessing steps using sample data
- Selecting features useful to the model
- Model selection
- Metrics and classification evaluation
- Regression examples using k-nearest neighbor, decision trees, boosting, and more
- Metrics for regression evaluation
- Clustering
- Dimensionality reduction
- Scikit-learn pipelines
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Matt runs MetaSnake, a Python and Data Science training and consulting company. He has over 15 years of experience using Python across a breadth of domains: Data Science, BI, Storage, Testing and Automation, Open Source Stack Management, and Search.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Machine Learning Pocket Reference: Working with Structured Data in Python
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O'Reilly Media (edition 1), 2019
ISBN 10 : 1492047546
ISBN 13 : 9781492047544
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Paperback. Etat : As New. 1. It's a preowned item in almost perfect condition. It has no visible cosmetic imperfections. May come without any shrink wrap; pages are clean and not marred by notes or folds of any kind. N° de réf. du vendeur 1492047546-10-1
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Machine Learning Pocket Reference
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Machine Learning Pocket Reference
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Paperback. Etat : New. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines. N° de réf. du vendeur LU-9781492047544
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Machine Learning Pocket Reference
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Machine Learning Pocket Reference
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Paperback. Etat : New. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines. N° de réf. du vendeur LU-9781492047544
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MACHINE LEARNING POCKET REFERENCE
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Machine Learning Pocket Reference
Vendeur : Rarewaves.com UK, London, Royaume-Uni
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Paperback. Etat : New. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines. N° de réf. du vendeur LU-9781492047544
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Machine Learning Pocket Reference: Working with Structured Data in Python
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Machine Learning Pocket Reference: Working with Structured Data in Python
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Etat : New. 2019. Revised. Paperback. . . . . . N° de réf. du vendeur V9781492047544
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Machine Learning Pocket Reference: Working with Structured Data in Python
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