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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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Tshepo, Chris Nokeri
Format: Electronic eBook
Language:Inglés
Published: [United States] : Apress, 2022.
Subjects:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
Machine learning
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • 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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