Machine learning with Python cookbook: practical solutions from preprocessing to deep learning
Contents: Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection --...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing
O'Reilly
April 2018
|
| Ausgabe: | first edition |
| Schlagworte: | |
| Online-Zugang: | Ausführliche Beschreibung |
| Zusammenfassung: | Contents: Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models |
| Beschreibung: | xiii, 349 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491989388 1491989386 |
Internformat
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| 505 | 8 | |a Summary: With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher | |
| 520 | |a Contents: Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models | ||
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Datensatz im Suchindex
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| author | Albon, Chris |
| author_GND | (DE-588)1165271796 |
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| contents | Summary: With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher |
| ctrlnum | (OCoLC)1050952111 (DE-599)BVBBV045146018 |
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| dewey-hundreds | 000 - Computer science, information, general works |
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| discipline | Informatik |
| edition | first edition |
| format | Book |
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| id | DE-604.BV045146018 |
| illustrated | Illustrated |
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| isbn | 9781491989388 1491989386 |
| language | English |
| lccn | 2017278943 |
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| spelling | Albon, Chris (DE-588)1165271796 aut Machine learning with Python cookbook practical solutions from preprocessing to deep learning Chris Albon first edition Beijing O'Reilly April 2018 xiii, 349 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Summary: With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher Contents: Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models Machine learning Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe, PDF 978-1-491-98933-3 (DE-604)BV045143615 text/html https://www.amazon.de/Python-Machine-Learning-Cookbook-preprocessing/dp/1491989386/ref=sr_1_1?s=books-intl-de&ie=UTF8&qid=1543578586&sr=1-1&keywords=9781491989388 Ausführliche Beschreibung |
| spellingShingle | Albon, Chris Machine learning with Python cookbook practical solutions from preprocessing to deep learning Summary: With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher Machine learning Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4193754-5 |
| title | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_auth | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_exact_search | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_full | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Chris Albon |
| title_fullStr | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Chris Albon |
| title_full_unstemmed | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Chris Albon |
| title_short | Machine learning with Python cookbook |
| title_sort | machine learning with python cookbook practical solutions from preprocessing to deep learning |
| title_sub | practical solutions from preprocessing to deep learning |
| topic | Machine learning Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Python (Computer program language) Python Programmiersprache Maschinelles Lernen |
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