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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Bibliographic Details
Main Author Tshepo, Chris Nokeri
Format Electronic eBook
LanguageEnglish
Published [United States] : Apress, 2022.
Subjects
Machine learning.
Python (Computer program language)
elektronické knihy
electronic books
Online AccessFull text
ISBN9781484277621
1484277627
1484277619
9781484277614
Physical Description1 online resource

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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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