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Machine learning with Python cookbook : practical solutions from preprocessing to deep learning /
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 c...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2018]
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Albon, Chris, |e author. | |
| 245 | 1 | 0 | |a Machine learning with Python cookbook : |b practical solutions from preprocessing to deep learning / |c Chris Albon. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c [2018] | |
| 264 | 4 | |c Ã2018 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 588 | 0 | |a Online resource; title from title page (Safari, viewed March 19, 2018). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a Preface -- Chapter 1. Vectors, Matrices, and Arrays -- Chapter 2. Loading Data -- Chapter 3. Data Wrangling -- Chapter 4. Handling Numerical Data -- Chapter 5. Handling Categorical Data -- Chapter 6. Handling Text -- Chapter 7. Handling Dates and Times -- Chapter 8. Handling Images -- Chapter 9. Dimensionality Reduction Using Feature Extraction -- Chapter 10. Dimensionality Reduction Using Feature Selection -- Chapter 11. Model Evaluation -- Chapter 12. Model Selection -- Chapter 13. Linear Regression -- Chapter 14. Trees and Forests -- Chapter 15. K-Nearest Neighbors -- Chapter 16. Logistic Regression -- Chapter 17. Support Vector Machines -- Chapter 18. Naive Bayes -- Chapter 19. Clustering -- Chapter 20. Neural Networks -- Chapter 21. Saving and Loading Trained Models. | |
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| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
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