最新章節

• Summary
• Identifying student groups using fuzzy clustering
• Classifying thyroid disease with a neural network
• Data fitting for predicting the quality of concrete
• Machine Learning in Practice
• Summary

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

• 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 更新時間：2021-07-02 19:38:04