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Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn By Tshepo Chris Nokeri

By: Material type: TextTextPublication details: Apress, c2022.Edition: 1Description: i-xvi+119PISBN:
  • 9781484283509
Subject(s): DDC classification:
  • 1 006.31 NOK-D
Online resources:
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
Summary: Apply supervised and unsupervised learning to solve practical and real-world big data problems
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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics 006.31 NOK-D (Browse shelf(Opens below)) Available DCB4164

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

Apply supervised and unsupervised learning to solve practical and real-world big data problems

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