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Mastering Machine Learning with R > PACKT 원서리스트

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Mastering Machine Learning with R
판매가격 39,000원
저자 Lesmeister
도서종류 외국도서
출판사 PACKT
발행언어 영어
발행일 2015-10
페이지수 400
ISBN 9781783984527
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보조자료 다운
  • 도서 정보

    도서 상세설명

    1: A Process for Success
    The process
    Business understanding
    Data understanding
    Data preparation
    Modeling
    Evaluation
    Deployment
    Algorithm flowchart
    Summary

    2: Linear Regression – The Blocking and Tackling of Machine Learning
    Univariate linear regression
    Multivariate linear regression
    Other linear model considerations
    Summary

    3: Logistic Regression and Discriminant Analysis
    Classification methods and linear regression
    Logistic regression
    Model selection
    Summary

    4: Advanced Feature Selection in Linear Models
    Regularization in a nutshell
    Business case
    Modeling and evaluation
    Model selection
    Summary

    5: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
    K-Nearest Neighbors
    Support Vector Machines
    Business case
    Feature selection for SVMs
    Summary

    6: Classification and Regression Trees
    Introduction
    An overview of the techniques
    Business case
    Summary

    7: Neural Networks
    Neural network
    Deep learning, a not-so-deep overview
    Business understanding
    Data understanding and preparation
    Modeling and evaluation
    An example of deep learning
    Summary

    8: Cluster Analysis
    Hierarchical clustering
    K-means clustering
    Gower and partitioning around medoids
    Data understanding and preparation
    Modeling and evaluation
    Summary

    9: Principal Components Analysis
    An overview of the principal components
    Modeling and evaluation
    Summary

    10: Market Basket Analysis and Recommendation Engines
    An overview of a market basket analysis
    Business understanding
    Data understanding and preparation
    Modeling and evaluation
    An overview of a recommendation engine
    Business understanding and recommendations
    Data understanding, preparation, and recommendations
    Modeling, evaluation, and recommendations
    Summary

    11: Time Series and Causality
    Univariate time series analysis
    Modeling and evaluation
    Summary

    12: Text Mining
    Text mining framework and methods
    Topic models
    Modeling and evaluation
    Summary

    Appendix A: R Fundamentals
    Introduction
    Getting R up and running
    Using R
    Data frames and matrices
    Summary stats
    Installing and loading the R packages
    Summary

    Appendix B: Index
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