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Developing classification and regression systems /
Code-along sessions move you through the development of classification and regression methods. 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. Developing Classification and Re...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Boston, Massachusetts] :
Addison-Wesley Professional,
[2022]
|
| Edición: | [First edition]. |
| Colección: | LiveLessons (Boston, Massachusetts)
Machine learning with Python for everyone ; part 3. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Code-along sessions move you through the development of classification and regression methods. 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. Developing Classification and Regression Systems LiveLessons (Machine Learning with Python for Everyone Series) Part 3 shows you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. You will learn about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You will see techniques for feature engineering, including scaling, discretization, and interactions. You will learn how to implement pipelines for more complex processing and nested cross-validation for tuning hyperparameters. | ||
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