Java开发者机器学习实战指南：涵盖Weka、Spark与深度学习

机器学习

人工智能

Java

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"本书《Machine Learning End-to-End Guide for Java Developers》是针对Java开发者的一份全面指南，涵盖了从基础的Java数据科学到高级的Java机器学习技术。它旨在帮助开发者利用算法和数据让计算机实现类似人类的学习和行动。书中分为三个模块，详细介绍了Java在数据科学中的应用、Java中的机器学习库以及深入的Java机器学习技术。在模块1 - Java for Data Science中，探讨了Java对低级数学运算的支持，特别是在多处理器环境中的应用。数据分析的核心是对大量数值数据的处理和分析，这部分讲解了如何使用Java进行这些操作。模块2 - Machine Learning in Java，回顾了各种Java机器学习库和平台，如Weka、Java-ML、Apache Mahout、Apache Spark、deeplearning4j和Mallet。每个库的特点、应用场景以及它们能解决的问题都有所涉及。这些库覆盖了从基本的机器学习任务到深度学习的不同领域。模块3 - Mastering Java Machine Learning，深入研究了聚类和离群值检测的高级方法，包括无监督数据的特征选择和减少、聚类算法、聚类评估方法以及使用统计、距离和分布技术的异常检测。本模块通过MNIST图像数据集的案例研究，实践了聚类和离群值检测，并使用Smile API进行特征降维，ELKI进行学习。此外，书中的内容还包括了数据科学解决问题的方法，数据获取的重要性与清洗过程，数据可视化以增强理解，统计方法在数据科学中的应用，机器学习在数据科学中的应用，神经网络和深度学习，文本分析，视觉和音频分析，以及使用并行技术优化应用程序性能。总体而言，这本书提供了一个全面的路径，引导Java开发者从入门到精通机器学习，通过理论与实践的结合，提升在AI、机器学习和人工智能领域的专业能力。"

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Importance of evaluation
Getting models into production
Model maintenance
Standards and markup languages
CRISP-DM
SEMMA methodology
Predictive Model Markup Language
Machine learning in the cloud
Machine learning as a service
Web resources and competitions
Datasets
Online courses
Competitions
Websites and blogs
Venues and conferences
Summary
A. References
3. Module 3
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
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
Categorical features
Continuous features
Multivariate feature analysis
Data transformation and preprocessing
Feature construction
Handling missing values
Outliers
Discretization
Data sampling
Is sampling needed?
Undersampling and oversampling
Stratified sampling
Training, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniques
Feature evaluation techniques
Filter approach
Univariate feature selection
Information theoretic approach
Statistical approach
Multivariate feature selection
Minimal redundancy maximal relevance (mRMR)
Correlation-based feature selection (CFS)
Wrapper approach
Embedded approach
Model building
Linear models
Linear Regression
Algorithm input and output
How does it work?
Advantages and limitations
Naïve Bayes
Algorithm input and output
How does it work?
Advantages and limitations
Logistic Regression

Algorithm input and output
How does it work?
Advantages and limitations
Non-linear models
Decision Trees
Algorithm inputs and outputs
How does it work?
Advantages and limitations
K-Nearest Neighbors (KNN)
Algorithm inputs and outputs
How does it work?
Advantages and limitations
Support vector machines (SVM)
Algorithm inputs and outputs
How does it work?
Advantages and limitations
Ensemble learning and meta learners
Bootstrap aggregating or bagging
Algorithm inputs and outputs
How does it work?
Random Forest
Advantages and limitations
Boosting
Algorithm inputs and outputs
How does it work?
Advantages and limitations
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
McNemar's Test
Paired-t test
Wilcoxon signed-rank test
Comparing multiple algorithms

ANOVA test
Friedman's test
Case Study – Horse Colic Classification
Business problem
Machine learning mapping
Data analysis
Label analysis
Features analysis
Supervised learning experiments
Weka experiments
Sample end-to-end process in Java
Weka experimenter and model selection
RapidMiner experiments
Visualization analysis
Feature selection
Model process flow
Model evaluation metrics
Evaluation on Confusion Metrics
ROC Curves, Lift Curves, and Gain Charts
Results, observations, and analysis
Summary
References
3. Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Notation
Linear methods
Principal component analysis (PCA)
Inputs and outputs
How does it work?
Advantages and limitations
Random projections (RP)
Inputs and outputs
How does it work?
Advantages and limitations
Multidimensional Scaling (MDS)
Inputs and outputs

How does it work?
Advantages and limitations
Nonlinear methods
Kernel Principal Component Analysis (KPCA)
Inputs and outputs
How does it work?
Advantages and limitations
Manifold learning
Inputs and outputs
How does it work?
Advantages and limitations
Clustering
Clustering algorithms
k-Means
Inputs and outputs
How does it work?
Advantages and limitations
DBSCAN
Inputs and outputs
How does it work?
Advantages and limitations
Mean shift
Inputs and outputs
How does it work?
Advantages and limitations
Expectation maximization (EM) or Gaussian mixture modeling (GMM)
Input and output
How does it work?
Advantages and limitations
Hierarchical clustering
Input and output
How does it work?
Advantages and limitations
Self-organizing maps (SOM)
Inputs and outputs
How does it work?
Advantages and limitations
Spectral clustering