Distributed Machine Learning with PySpark Migrating Effortlessly from Pandas and Scikit-Learn
Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Dist...
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Berkeley, CA :
Apress
2023.
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| Edition: | 1st ed. 2023. |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009786707106719 |
Table of Contents:
- Chapter 1: An Easy Transition
- Chapter 2: Selecting Algorithms
- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark
- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark
- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark
- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark
- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark
- Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark
- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark
- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark
- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark
- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark
- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark
- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark
- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark
- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark
- Chapter 17: Pipelines with Scikit-Learn and PySpark
- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark. .
