文件列表:

Chapter02\apollo.wav (127098, 2017-12-17)
Chapter02\capri.xlsx (11860, 2017-12-17)
Chapter02\coliseum.jpg (292010, 2017-12-17)
Chapter02\DataOrganization.m (1485, 2017-12-17)
Chapter02\Exporting.m (452, 2017-12-17)
Chapter02\Ferrari.txt (152, 2017-12-17)
Chapter02\Importing.m (611, 2017-12-17)
Chapter02\matrix.csv (26, 2017-12-17)
Chapter02\matrix.txt (26, 2017-12-17)
Chapter02\museum.xls (32768, 2017-12-17)
Chapter02\MyMatrix.csv (199, 2017-12-17)
Chapter02\MyMatrix.txt (202, 2017-12-17)
Chapter02\MyMatrix.xls (17920, 2017-12-17)
Chapter03\CleaningData.m (991, 2017-12-17)
Chapter03\CleaningData.xlsx (10966, 2017-12-17)
Chapter03\EmergencyCalls.xlsx (8490, 2017-12-17)
Chapter03\EmergencyCallsNew.xlsx (8503, 2017-12-17)
Chapter03\ExploratoryStatistics.m (1139, 2017-12-17)
Chapter03\ExploratoryVisualization.m (1967, 2017-12-17)
Chapter03\GlassIdentificationDataSet.xlsx (25320, 2017-12-17)
Chapter03\LifeExpectancy.xlsx (8532, 2017-12-17)
Chapter04\AirfoilSelfNoise.csv (373704, 2017-12-17)
Chapter04\employees.xlsx (11827, 2017-12-17)
Chapter04\RegressionExample.m (3353, 2017-12-17)
Chapter04\VehiclesItaly.xlsx (10504, 2017-12-17)
Chapter05\DecisionTrees.m (573, 2017-12-17)
Chapter05\DiscriminantAnalysis.m (1243, 2017-12-17)
Chapter05\NaiveBayes.m (751, 2017-12-17)
Chapter05\NearestNeighborCassifiers.m (712, 2017-12-17)
Chapter06\ClimaticData.xls (109056, 2017-12-17)
Chapter06\GaussianMixtureModels.m (615, 2017-12-17)
Chapter06\HierarchicalClustering.m (408, 2017-12-17)
Chapter06\KMeansExample.m (500, 2017-12-17)
Chapter06\KMedoids Example.m (355, 2017-12-17)
Chapter06\Minerals.xls (162304, 2017-12-17)
Chapter06\PeripheralLocations.xls (172032, 2017-12-17)
Chapter07\EngineFitting.m (542, 2017-12-17)
Chapter08\PrincipalComponentAnalysis.m (882, 2017-12-17)
Chapter08\seeds_dataset.csv (9300, 2017-12-17)
Chapter08\StepwiseRegression.m (1741, 2017-12-17)
... ...

# MATLAB for Machine Learning This is the code repository for [MATLAB for Machine Learning](https://www.packtpub.com/big-data-and-business-intelligence/matlab-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=97817883***435), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the book from start to finish. ## About the Book MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll also explore classification techniques such as K-nearest neighbor analysis and Naive Bayes algorithm, and understand decision tree and rules learners. After this, you’ll dive into unsupervised learning and find groups of data with clustering methods such as k-means method and dendrogram. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. ##Instructions and Navigation All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. The code will look like the following: ``` PC1 = 0.8852* Area + 0.3958 * Perimeter + 0.0043 * Compactness + 0.1286 * LengthK + 0.1110 * WidthK - 0.1195 * AsymCoef + 0.1290 * LengthKG ``` Any command-line input or output is written as follows: ``` >>10+90 ans = 100 ``` In this book, machine learning algorithms are implemented in the MATLAB environment. So, to reproduce the many examples in this book, you need a new version of MATLAB (R2017a recommended) and the following toolboxes: statistics and machine learning toolbox, neural network toolbox, and fuzzy logic toolbox. ## Related Products * [Visual Media Processing Using MATLAB Beginner's Guide](https://www.packtpub.com/hardware-and-creative/visual-media-processing-using-matlab-beginners-guide?utm_source=github&utm_medium=repository&utm_campaign=9781849697200) * [MATLAB Graphics and Data Visualization Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/matlab-graphics-and-data-visualization-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781849693165) * [Rapid BeagleBoard Prototyping with MATLAB and Simulink](https://www.packtpub.com/hardware-and-creative/rapid-beagleboard-prototyping-matlab-and-simulink?utm_source=github&utm_medium=repository&utm_campaign=9781849696043)

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