Applied Unsupervised Learning with R
暫譯: 使用 R 的應用無監督學習

Alok Malik , Bradford Tuckfield

Applied Unsupervised Learning with R

商品描述

Key Features

  • Build state-of-the-art algorithms that can solve your business' problems
  • Learn how to find hidden patterns in your data
  • Revise key concepts with hands-on exercises using real-world datasets

Book Description

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions.

This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models.

By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

What you will learn

  • Implement clustering methods such as k-means, agglomerative, and divisive
  • Write code in R to analyze market segmentation and consumer behavior
  • Estimate distribution and probabilities of different outcomes
  • Implement dimension reduction using principal component analysis
  • Apply anomaly detection methods to identify fraud
  • Design algorithms with R and learn how to edit or improve code

Who this book is for

Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

商品描述(中文翻譯)

**主要特點**

- 建立最先進的演算法，以解決您的業務問題
- 學習如何在數據中尋找隱藏的模式
- 通過使用真實世界數據集的實作練習來複習關鍵概念

**書籍描述**

《應用無監督學習與 R》從基礎開始，解釋了聚類方法、分佈分析、數據編碼器以及 R 的特性，這些都能幫助您更好地理解數據並獲得對您最迫切的業務問題的答案。

本書首先介紹了無監督學習中最重要且最常用的方法——聚類，並解釋了三種主要的聚類演算法——k-means、分裂式和凝聚式。接下來，您將學習市場籃分析、核密度估計、主成分分析和異常檢測。您將通過使用 R 編寫的代碼來了解這些方法，並獲得進一步的指導，了解如何處理、編輯和改進 R 代碼。為了幫助您獲得實際的理解，本書還提供了將這些方法應用於真實業務問題的有用提示，包括市場細分和詐騙檢測。通過參與有趣的活動，您將探索數據編碼器和潛變量模型。

在本書結束時，您將對不同的異常檢測方法有更好的理解，例如離群點檢測、Mahalanobis 距離以及上下文和集體異常檢測。

**您將學到什麼**

- 實現聚類方法，如 k-means、凝聚式和分裂式
- 使用 R 編寫代碼來分析市場細分和消費者行為
- 估計不同結果的分佈和概率
- 使用主成分分析實現降維
- 應用異常檢測方法以識別詐騙
- 使用 R 設計演算法，並學習如何編輯或改進代碼

**本書適合誰**

《應用無監督學習與 R》是為希望學習如何更好地理解數據的方法的商業專業人士以及對無監督學習感興趣的開發人員而設計的。雖然本書適合初學者，但對 R 有一些基本的初學者級別的熟悉度會更有幫助。這包括了解如何打開 R 控制台、如何讀取數據以及如何創建循環。為了更容易理解本書的概念，您還應該了解基本的數學概念，包括指數、平方根、平均數和中位數。

作者簡介

Bradford Tuckfield is the Principal Data Scientist for Xtage Labs, a data science consulting company. He has years of experience with creating and deploying unsupervised learning solutions in fields as diverse as finance, real estate, corporate travel, and media. He has a Ph.D. from the Wharton School of the University of Pennsylvania, where he studied economics and statistics, and a B.S. from Brigham Young University, where he studied mathematics. He has published research on linear algebra and public policy in scholarly journals and has also written for the popular press.

Alok Malik is a Data Scientist based in India. He has previously worked on creating and deploying unsupervised learning solutions in fields such as finance, cryptocurrency trading, logistics and natural language processing. He is a B.Tech graduate from the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, where he studied electronics and communication engineering.

 

作者簡介(中文翻譯)

Bradford Tuckfield 是 Xtage Labs 的首席數據科學家，這是一家數據科學諮詢公司。他在金融、房地產、企業差旅和媒體等多個領域擁有多年創建和部署無監督學習解決方案的經驗。他擁有賓夕法尼亞大學沃頓商學院的博士學位，專攻經濟學和統計學，以及布里格姆楊大學的學士學位，主修數學。他在學術期刊上發表過有關線性代數和公共政策的研究，並且也為大眾媒體撰寫過文章。

Alok Malik 是一位駐印度的數據科學家。他曾在金融、加密貨幣交易、物流和自然語言處理等領域創建和部署無監督學習解決方案。他是印度信息技術、設計與製造學院（Indian Institute of Information Technology, Design and Manufacturing, Jabalpur）的 B.Tech 畢業生，主修電子與通信工程。

目錄大綱

Table of Contents

  1. Introduction to Clustering Methods
  2. Advanced Clustering Methods
  3. Probability Distributions
  4. Dimension Reduction
  5. Data Comparison Methods
  6. Anomaly Detection

目錄大綱(中文翻譯)

Table of Contents


  1. Introduction to Clustering Methods

  2. Advanced Clustering Methods

  3. Probability Distributions

  4. Dimension Reduction

  5. Data Comparison Methods

  6. Anomaly Detection

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