Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio
暫譯: 文本的機器學習技術:使用 Python 應用現代技術進行文本處理、降維、分類和評估

Tsourakis, Nikos

  • 出版商: Packt Publishing
  • 出版日期: 2022-10-31
  • 售價: $1,650
  • 貴賓價: 9.5$1,568
  • 語言: 英文
  • 頁數: 448
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1803242388
  • ISBN-13: 9781803242385
  • 相關分類: Python程式語言Machine Learning
  • 立即出貨 (庫存=1)

商品描述

Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guide


Key Features:

  • Learn how to acquire and process textual data and visualize the key findings
  • Obtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffs
  • Implement models for solving real-world problems and evaluate their performance


Book Description:

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code.

A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions.

By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.


What You Will Learn:

  • Understand fundamental concepts of machine learning for text
  • Discover how text data can be represented and build language models
  • Perform exploratory data analysis on text corpora
  • Use text preprocessing techniques and understand their trade-offs
  • Apply dimensionality reduction for visualization and classification
  • Incorporate and fine-tune algorithms and models for machine learning
  • Evaluate the performance of the implemented systems
  • Know the tools for retrieving text data and visualizing the machine learning workflow


Who this book is for:

This book is for professionals in the area of computer science, programming, data science, informatics, business analytics, statistics, language technology, and more who aim for a gentle career shift in machine learning for text. Students in relevant disciplines that seek a textbook in the field will benefit from the practical aspects of the content and how the theory is presented. Finally, professors teaching a similar course will be able to pick pertinent topics in terms of content and difficulty. Beginner-level knowledge of Python programming is needed to get started with this book.

商品描述(中文翻譯)

提升您的 Python 文本處理技能,透過這本全彩指南學習最新的自然語言處理和機器學習技術

主要特點:


  • 學習如何獲取和處理文本數據,並可視化關鍵發現

  • 深入了解最常用的算法和技術,並理解它們的權衡

  • 實現解決現實世界問題的模型並評估其性能

書籍描述:
隨著對機器學習和程式設計專業人士需求的日益增加,現在正是投資這一領域的最佳時機。本書將幫助您專注於文本數據和人類語言,並在各種教科書中呈現複雜的理論概念或過度專注於 Python 代碼之間找到平衡。

這本書所建立的一個良好隱喻是經驗豐富的工匠與其學徒之間的關係。根據當前的問題,前者從工具箱中選擇一個工具,解釋其用途並付諸實踐。這種方法將幫助您為每種方法或技術識別至少一個實際用途。本書內容分為十個章節,每個章節討論一個特定的案例研究。因此,本書是以解決方案為導向的。書中附有 Jupyter notebooks 形式的 Python 代碼,以幫助您獲得實踐經驗。本書章節中的一個重複模式是幫助您對數據獲得一些直覺,然後實施和對比各種解決方案。

在本書結束時,您將能夠理解並應用各種技術,使用 Python 進行文本預處理、文本表示、降維、機器學習、語言建模、可視化和評估。

您將學到的內容:


  • 理解文本的機器學習基本概念

  • 發現文本數據如何被表示並建立語言模型

  • 對文本語料庫進行探索性數據分析

  • 使用文本預處理技術並理解其權衡

  • 應用降維技術進行可視化和分類

  • 整合和微調機器學習的算法和模型

  • 評估實施系統的性能

  • 了解檢索文本數據和可視化機器學習工作流程的工具

本書適合誰:
本書適合計算機科學、程式設計、數據科學、資訊學、商業分析、統計學、語言技術等領域的專業人士,旨在為文本的機器學習進行輕鬆的職業轉型。相關學科的學生尋求該領域的教科書將受益於內容的實用性及理論的呈現方式。最後,教授類似課程的教師將能夠選擇與內容和難度相關的主題。開始閱讀本書需要具備初級的 Python 程式設計知識。