Data Science Solutions with Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn

• Autore: Nokeri Tshepo Chris
• Pubblicazione: Berkeley, CA : , : Apress L. P., , 2021; ©2022
• Descrizione fisica: 1 online resource (128 pages)
• Disciplina: 006.31
• Soggetto genere / forma: Electronic books.
• Nota di contenuto: Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Exploring Machine Learning -- Exploring Supervised Methods -- Exploring Nonlinear Models -- Exploring Ensemble Methods -- Exploring Unsupervised Methods -- Exploring Cluster Methods -- Exploring Dimension Reduction -- Exploring Deep Learning -- Conclusion -- Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks -- Big Data -- Big Data Features -- Impact of Big Data on Business and People -- Better Customer Relationships -- Refined Product Development -- Improved Decision-Making -- Big Data Warehousing -- Big Data ETL -- Big Data Frameworks -- Apache Spark -- Resilient Distributed Data Sets -- Spark Configuration -- Spark Frameworks -- SparkSQL -- Spark Streaming -- Spark MLlib -- GraphX -- ML Frameworks -- Scikit-Learn -- H2O -- XGBoost -- DL Frameworks -- Keras -- Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O -- Exploring the Ordinary Least-Squares Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 4: Survival Analysis with PySpark and Lifelines -- Exploring Survival Analysis -- Exploring Cox Proportional Hazards Method -- Lifeline in Action -- Exploring the Accelerated Failure Time Method -- PySpark in Action -- Conclusion -- Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O -- Exploring the Logistic Regression Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O -- Decision Trees -- Preprocessing Features -- Scikit-Learn in Action -- Gradient Boosting -- XGBoost in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O. ; Exploring Deep Learning -- Multilayer Perceptron Neural Network -- Preprocessing Features -- Scikit-Learn in Action -- Keras in Action -- Deep Belief Networks -- H2O in Action -- Conclusion -- Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H2O -- Exploring the K-Means Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H2O -- Exploring the Principal Component Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 10: Automating the Machine Learning Process with H2O -- Exploring Automated Machine Learning -- Preprocessing Features -- H2O AutoML in Action -- Conclusion -- Index.
• Titolo autorizzato: Data Science Solutions with Python
• ISBN: 1-4842-7762-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910506385603321
• Lo trovi qui: Univ. Federico II