cover
Title Page
Copyright
MATLAB for Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Getting Started with MATLAB Machine Learning
ABC of machine learning
Discover the different types of machine learning
Supervised learning
Unsupervised learning
Reinforcement learning
Choosing the right algorithm
How to build machine learning models step by step
Introducing machine learning with MATLAB
System requirements and platform availability
MATLAB ready for use
Statistics and Machine Learning Toolbox
Datatypes
Supported datatypes
Unsupported datatypes
What can you do with the Statistics and Machine Learning Toolbox?
Data mining and data visualization
Regression analysis
Classification
Cluster analysis
Dimensionality reduction
Neural Network Toolbox
Statistics and algebra in MATLAB
Summary
Importing and Organizing Data in MATLAB
Familiarizing yourself with the MATLAB desktop
Importing data into MATLAB
The Import Wizard
Importing data programmatically
Loading variables from file
Reading an ASCII-delimited file
Comma-separated value files
Importing spreadsheets
Reading mixed strings and numbers
Exporting data from MATLAB
Working with media files
Handling images
Sound import/export
Data organization
Cell array
Structure array
Table
Categorical array
Summary
From Data to Knowledge Discovery
Distinguishing the types of variables
Quantitative variables
Qualitative variables
Data preparation
A first look at data
Finding missing values
Changing the datatype
Replacing the missing value
Removing missing entries
Ordering the table
Finding outliers in data
Organizing multiple sources of data into one
Exploratory statistics - numerical measures
Measures of location
Mean median and mode
Quantiles and percentiles
Measures of dispersion
Measures of shape
Skewness
Kurtosis
Exploratory visualization
The Data Statistics dialog box
Histogram
Box plots
Scatter plots
Summary
Finding Relationships between Variables - Regression Techniques
Searching linear relationships
Least square regression
The Basic Fitting interface
How to create a linear regression model
Reducing outlier effects with robust regression
Multiple linear regression
Multiple linear regression with categorical predictor
Polynomial regression
Regression Learner App
Summary
Pattern Recognition through Classification Algorithms
Predicting a response by decision trees
Probabilistic classification algorithms - Naive Bayes
Basic concepts of probability
Classifying with Naive Bayes
Bayesian methodologies in MATLAB
Describing differences by discriminant analysis
Find similarities using nearest neighbor classifiers
Classification Learner app
Summary
Identifying Groups of Data Using Clustering Methods
Introduction to clustering
Similarity and dissimilarity measures
Methods for grouping objects
Hierarchical clustering
Partitioning clustering
Hierarchical clustering
Similarity measures in hierarchical clustering
Defining a grouping in hierarchical clustering
How to read a dendrogram
Verifying your hierarchical clustering
Partitioning-based clustering methods - K-means algorithm
The K-means algorithm
The kmeans() function
The silhouette plot
Partitioning around the actual center - K-medoids clustering
What is a medoid?
The kmedoids() function
Evaluating clustering
Clustering using Gaussian mixture models
Gaussian distribution
GMM in MATLAB
Cluster membership by posterior probabilities
Summary
Simulation of Human Thinking - Artificial Neural Networks
Getting started with neural networks
Basic elements of a neural network
The number of hidden layers
The number of nodes within each layer
The network training algorithm
Neural Network Toolbox
A neural network getting started GUI
Data fitting with neural networks
How to use the Neural Fitting app (nftool)
Script analysis
Summary
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Feature selection
Basics of stepwise regression
Stepwise regression in MATLAB
Feature extraction
Principal Component Analysis
Summary
Machine Learning in Practice
Data fitting for predicting the quality of concrete
Classifying thyroid disease with a neural network
Identifying student groups using fuzzy clustering
Summary
更新时间：2021-07-02 19:38:04
完结共161章
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