MATLAB for Machine Learning

Giuseppe Ciaburro

MATLAB for Machine Learning

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商品描述

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

商品描述(中文翻譯)

從你的數據中以簡單的方式使用MATLAB提取模式和知識

關於本書

- 通過這本易於理解的指南，初步了解機器學習
- 使用MATLAB學習回歸、聚類、分類、預測分析、人工神經網絡等知識
- 通過機器學習的力量，了解數據的運作方式並識別數據中的隱藏層次

本書適合對機器學習感興趣的數據分析師、數據科學家、學生或任何希望開始機器學習並構建高效數據處理和預測應用程序的人。具備數學和統計背景將有助於更好地理解本書內容。

你將學到什麼

- 學習機器學習的入門概念
- 通過SAS XPORT、導入和導出工具等不同方式來轉換數據
- 探索不同類型的回歸技術，如簡單線性回歸、多元線性回歸、普通最小二乘估計、相關性等，以及如何應用到你的數據中
- 在MATLAB環境中實現Naive Bayes算法和決策樹，了解分類方法的基礎知識
- 使用層次聚類等聚類方法來將數據分組
- 利用MATLAB神經網絡工具箱進行數據擬合、模式識別和聚類分析
- 學習特徵選擇和提取以降低維度並提高性能

詳細內容

MATLAB是首選的編程語言

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