本代码为即将发行的关于完整机器学习流程的电子书的完整代码。

This submission provides the code explained by the (upcoming) eBook on the complete machine learning workflow.

基于PhysioNet 2016挑战的心音记录，开发了一个模型，该模型将心音分为正常和异常，并在原型（心脏）筛选应用程序中予以实现。

Based on the heart sound recordings of the PhysioNet 2016 challenge, a model is developed that classifies heart sounds into normal vs abnormal, and deployed in a prototype (heart) screening application.

本代码验证了以下工作：

1. 使用数据存储方式有效地从多个文件夹读取大量数据文件

2. using datastore for efficiently reading large number of data files from several folders

3. 利用信号处理、小波和统计学中的工具进行特征提取

4. using tools from signal processing, wavelets and statistics for feature extraction

5. 使用ClassificationLearner应用程序训练并比较多个分类器

6. using ClassificationLearner app for training and comparing multiple classifiers

7. 以编程方式训练具有误分类代价的组合分类器

8. programmatically training an ensemble classifier with misclassification costs

9. 使用用于特征选择的邻域分量分析来选择相关特征的较小子集

10. using neighborhood component analysis for feature selection to select a smaller subset of relevant features

11. 执行C代码生成以完成嵌入式系统的设计

12. performing C code generation for deployment to an embedded system

源码下载地址：

http://page5.dfpan.com/fs/blcja2213291161f705/

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