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MATLAB for Machine Learning

Book Description

Extract patterns and knowledge from your data in easy way using MATLAB

About This Book

  • Get your first steps into machine learning with the help of this easy-to-follow guide
  • Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB
  • Understand how your data works and identify hidden layers in the data with the power of machine learning.

Who This Book Is For

This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.

What You Will Learn

  • Learn the introductory concepts of machine learning.
  • Discover different ways to transform data using SAS XPORT, import and export tools,
  • Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.
  • Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.
  • Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.
  • Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.
  • Learn feature selection and extraction for dimensionality reduction leading to improved performance.

In Detail

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 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.

Style and approach

The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Table of Contents

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