Data science solutions with Python : fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn

Data science solutions with Python fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distribute...

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Detalles Bibliográficos
Otros Autores: Nokeri, Tshepo Chris, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: [Place of publication not identified] : Apress [2022]
Materias:
Machine learning.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009635713306719
Tabla de Contenidos:
  • 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.

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