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精通Java機器學習(影印版)(英文版)

  • 作者:(印)烏代·卡馬特//克里希納·查普佩拉
  • 出版社:東南大學
  • ISBN:9787564178642
  • 出版日期:2018/10/01
  • 裝幀:平裝
  • 頁數:519
人民幣:RMB 108 元      售價:
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內容大鋼
    Java是從事實踐工作的數據科學家的主力語言,不少Hadoop生態系統都基於Java,數據科學領域中大多數生產系統絕對都是用其編寫的。如果你了解Java,烏代·卡馬特、克里希納·查普佩拉著的這本《精通Java機器學習(影印版)(英文版)》就是你邁向成為數據科學高級從業者的下一步。
    本書旨在為你介紹機器學習領域的一系列先進技術,包括分類、聚類、異常檢測、流學習、主動學習、半監督學習、概率圖建模、文本挖掘、深度學習、大數據批處理以及流機器學習。每章都附有說明性示例和真實案例研究,展示如何使用合理的方法和當前最好的Java工具來運用新學到的技術。
    閱讀完本書後,你將理解構建能夠解決任何領域中的數據科學問題的強大機器學習模型所需的工具和技術。

作者介紹
(印)烏代·卡馬特//克里希納·查普佩拉

目錄
Preface
Chapter 1: Machine Learning Review
  Machine learning - history and definition
  What is not machine learning?
    Machine learning - concepts and terminology
  Machine learning - types and subtypes
  Datasets used in machine learning
  Machine learning applications
  Practical issues in machine learning
  Machine learning - roles and process
    Roles
    Process
  Machine learning -tools and datasets
    Datasets
  Summary
Chapter 2: Practical Approach to Real-World Supervised Learning
  Formal description and notation
    Data quality analysis
    Descriptive data analysis
      Basic label analysis
      Basic feature analysis
    Visualization analysis
      Univariate feature analysis
      Multivariate feature analysis
  Data transformation and preprocessing
    Feature construction
    Handling missing values
    Outliers
    Discretization
    Data sampling
      Is sampling needed?
      Undersampling and oversampling
    Training, validation, and test set
  Feature relevance analysis and dimensionality reduction
    Feature search techniques
    Feature evaluation techniques
      Filter approach
      Wrapper approach
      Embedded approach
  Model building
    Linear models
      Linear Regression
      Naive Bayes
      Logistic Regression
    Non-linear models
      Decision Trees
      K-Nearest Neighbors (KNN)
      Support vector machines (SVM)
    Ensemble learning and meta learners
      Bootstrap aggregating or bagging

      Boosting
  Model assessment, evaluation, and comparisons
    Model assessment
    Model evaluation metrics
      Confusion matrix and related metrics
      ROC and PRC curves
      Gain charts and lift curves
    Model comparisons
      Comparing two algorithms
      Comparing multiple algorithms
  Case Study - Horse Colic Classification
    Business problem
    Machine learning mapping
    Data analysis
      Label analysis
      Features analysis
    Supervised learning experiments
      Weka experiments
      RapidMiner experiments
    Results, observations, and analysis
  Summary
  References
Chapter 3: Unsupervised Machine Learninq Techniques
  ……
Chapter 4: Semi-Supervised and Active Learning
Chapter 5: Real-Time Stream Machine Learning
Chapter 6: Probabilistic Graph Modeling
Chapter 7: Deep Learning
Chapter 8: Text Mining and Natural Language Processing
Chapter 9: Bia Data Machine Learnina - The Final Frontier
Appendix A: Linear Algebra
Appendix B: Probability
Index

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