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

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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
Clasificación:Libro Electrónico
Autor principal: Tshepo, Chris Nokeri
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [United States] : Apress, 2022.
Temas:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Chapter 1: Understanding Machine Learning and Deep Learning
  • Chapter 2: Big Data Frameworks and ML and DL Frameworks
  • Chapter 3: The Parametric Method Linear Regression
  • Chapter 4: Survival Regression Analysis.-Chapter 5:The Non-Parametric Method - Classification
  • Chapter 6:Tree-based Modelling and Gradient Boosting
  • Chapter 7: Artificial Neural Networks
  • Chapter 8: Cluster Analysis using K-Means
  • Chapter 9: Dimension Reduction Principal Components Analysis
  • Chapter 10: Automated Machine Learning.

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