Java是从事实践工作的数据科学家的主力语言，不少Hadoop生态系统都基于Java，数据科学领域中大多数生产系统绝对都是用其编写的。如果你了解Java，乌代·卡马特、克里希纳·查普佩拉著的这本《精通Java机器学习（影印版）（英文版）》就是你迈向成为数据科学高级从业者的下一步。
本书旨在为你介绍机器学习领域的一系列先进技术，包括分类、聚类、异常检测、流学习、主动学习、半监督学习、概率图建模、文本挖掘、深度学习、大数据批处理以及流机器学习。每章都附有说明性示例和真实案例研究，展示如何使用合理的方法和当前最好的Java工具来运用新学到的技术。
阅读完本书后，你将理解构建能够解决任何领域中的数据科学问题的强大机器学习模型所需的工具和技术。

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