Machine learning with Python cookbook practical solutions from preprocessing to deep learning
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing, [China] :
O'Reilly
2018.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630610306719 |
Tabla de Contenidos:
- 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.
