Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5, 3rd Edition

Cory Lesmeister

  • 出版商: Packt Publishing
  • 出版日期: 2019-01-31
  • 定價: $1,480
  • 售價: 8.0$1,184
  • 語言: 英文
  • 頁數: 354
  • 裝訂: Paperback
  • ISBN: 1789618002
  • ISBN-13: 9781789618006
  • 相關分類: R 語言Machine Learning
  • 立即出貨(限量) (庫存=1)

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商品描述

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications

Key Features

  • Build independent machine learning (ML) systems leveraging the best features of R 3.5
  • Understand and apply different machine learning techniques using real-world examples
  • Use methods such as multi-class classification, regression, and clustering

Book Description

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models.

This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models can be diagnosed and understood.

By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work.

What you will learn

  • Prepare data for machine learning methods with ease
  • Understand how to write production-ready code and package it for use
  • Produce simple and effective data visualizations for improved insights
  • Master advanced methods, such as Boosted Trees and deep neural networks
  • Use natural language processing to extract insights in relation to text
  • Implement tree-based classifiers, including Random Forest and Boosted Tree

Who this book is for

This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Table of Contents

  1. Preparing and Understanding Data
  2. Linear Regression
  3. Logistic Regression
  4. Advanced Feature Selection in Linear Models
  5. K-Nearest Neighbors and Support Vector Machines
  6. Tree-Based Classification
  7. Neural Networks and Deep Learning
  8. Creating Ensembles and Multiclass Methods
  9. Cluster Analysis
  10. Principal Component Analysis
  11. Association Analysis
  12. Time Series and Causality
  13. Text Mining
  14. Appendix A- Creating a Package

商品描述(中文翻譯)

保持更新,掌握解決數據分析和機器學習挑戰的專家技巧,從複雜項目中獲取洞察力,並增強應用程序的功能。

主要特點:
- 利用 R 3.5 的最佳功能構建獨立的機器學習(ML)系統
- 通過實際示例了解並應用不同的機器學習技術
- 使用多類分類、回歸和聚類等方法

書籍描述:
鑑於 R 無成本統計編程環境的日益普及,現在是開始應用機器學習(ML)於數據的最佳時機。本書將教授您在 ML 中的高級技術,並使用 R 3.5 的最新代碼。您將深入研究監督學習、無監督學習和強化學習算法的各種複雜特性,以設計高效且強大的 ML 模型。

這本新版書籍包含了來自不同領域的各種任務的新鮮示例。《使用 R 精通機器學習》首先向您展示如何快速操作數據並為分析做好準備。您將探索簡單和複雜的模型,並了解如何進行比較。您還將學習使用最新的庫支持,例如 TensorFlow 和 Keras-R,進行高級計算。此外,您還將探索複雜的主題,如自然語言處理(NLP)、時間序列分析和聚類,這將進一步提升您在開發應用程序方面的技能。每章都將幫助您使用實際示例實施高級 ML 算法。您甚至將介紹強化學習及其各種用例和模型。在結尾章節中,您將瞭解如何診斷和理解某些黑盒模型。

通過閱讀本書,您將具備在自己的項目或工作中應用 ML 技術的能力。

您將學到什麼:
- 輕鬆為機器學習方法準備數據
- 理解如何編寫適用於生產的代碼並封裝它以供使用
- 生成簡單有效的數據可視化以獲得更好的洞察力
- 掌握高級方法,如提升樹和深度神經網絡
- 使用自然語言處理從文本中提取洞察力
- 實施基於樹的分類器,包括隨機森林和提升樹

本書適合對象:
本書適合數據科學專業人士、機器學習工程師或任何希望實施高級機器學習算法的人。本書將幫助您將技能提升到更高水平,並在這個領域取得更大的進步。需要具備 R 的機器學習工作知識。

目錄:
1. 數據的準備和理解
2. 線性回歸
3. 邏輯回歸
4. 線性模型中的高級特徵選擇
5. K-最近鄰和支持向量機
6. 基於樹的分類
7. 神經網絡和深度學習
8. 創建集成和多類方法
9. 聚類分析
10. 主成分分析
11. 關聯分析
12. 時間序列和因果關係
13. 文本挖掘
14. 附錄 A- 創建封裝