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MATLAB for Machine Learning
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Summary
Thisbookisfordataanalysts,datascientists,students,oranyonewhoislookingtogetstartedwithmachinelearningandwanttobuildefficientdataprocessingandpredictingapplications.Amathematicalandstatisticalbackgroundwillreallyhelpinfollowingthisbookwell.
目錄(161章)
倒序
- 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(shí)間：2021-07-02 19:38:04
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