Cargando…
MATLAB for Machine Learning.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: 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 datatypesUnsupported 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; Chapter 2: 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 numbersExporting data from MATLAB; Working with media files; Handling images; Sound import/export; Data organization; Cell array; Structure array; Table; Categorical array ; Summary; Chapter 3: 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 measuresMeasures 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; Chapter 4: 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 predictorPolynomial regression; Regression Learner App; Summary; Chapter 5: 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; Chapter 6: Identifying Groups of Data Using Clustering Methods; Introduction to clustering.