MATLAB for Machine Learning - Second Edition: Unlock the power of deep learning for swift and enhanced results

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications

Key Features:

• Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms
• Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring
• Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.

By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions.

This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks.

By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios.

What You Will Learn:

• Discover different ways to transform data into valuable insights
• Explore the different types of regression techniques
• Grasp the basics of classification through Naive Bayes and decision trees
• Use clustering to group data based on similarity measures
• Perform data fitting, pattern recognition, and cluster analysis
• Implement feature selection and extraction for dimensionality reduction
• Harness MATLAB tools for deep learning exploration

Who this book is for:

This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.

透過有效的程式編寫，以實際範例展示機器學習在現實應用中的多功能性，掌握使用MATLAB工具創建機器學習應用的技巧。

主要特點：
- 使用MATLAB機器學習工具箱實現各種機器學習算法
- 評估、部署和操作自定義模型，包括偏差檢測和管道監控
- 探索計算機視覺、時間序列分析和預測等深度學習的有效方法
- 購買印刷版或Kindle電子書，可獲得免費PDF電子書

書籍描述：
透過這本指南，了解為什麼研究人員和數學專家高度青睞MATLAB編程環境進行機器學習，提升您在機器學習和深度學習方面的能力，並為高級應用鋪平道路。

通過在MATLAB環境中使用多功能的機器學習工具，您將學習如何與工作空間無縫互動。然後，您將進行數據清理、數據挖掘和分析各種類型的機器學習數據，並在圖表上可視化數據值。隨著學習的進展，您將探索各種分類和回歸技術，並熟練地應用它們與MATLAB函數。

本書教授您神經網絡的基本知識，引導您進行數據擬合、模式識別和聚類分析。您還將探索特徵選擇和提取技術，以實現通過降維改善性能。最後，您將利用MATLAB工具進行深度學習和管理卷積神經網絡。

通過閱讀本書，您將能夠在現實場景中應用主要的機器學習算法。

您將學到什麼：
- 發現將數據轉化為有價值洞察的不同方法
- 探索不同類型的回歸技術
- 通過Naive Bayes和決策樹掌握分類的基礎知識
- 使用聚類根據相似度度量將數據分組
- 執行數據擬合、模式識別和聚類分析
- 實施特徵選擇和提取以實現降維
- 利用MATLAB工具進行深度學習探索

本書適合對象：
本書適合機器學習工程師、數據科學家、深度學習工程師和計算機視覺/自然語言處理工程師，他們希望使用MATLAB進行機器學習和深度學習。需要具備基本的編程概念的基礎知識才能開始閱讀。