Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio

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.

將你的Python文本處理技能提升到另一個水平，通過學習最新的自然語言處理和機器學習技術，這本全彩指南將幫助你實現這一目標。

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，並了解它們的優缺點
- 實施解決現實問題的模型並評估其性能

書籍描述：
隨著對機器學習和編程專業人員的需求不斷增加，現在是投資於這個領域的最佳時機。本書專注於文本數據和人類語言，避免了其他教科書中呈現複雜理論概念或過度關注Python代碼的問題。

本書以經驗豐富的工匠和學徒之間的關係為基礎，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，並將其應用到實際操作中。這種方法將幫助你確定每種方法或技術的至少一個實際用途。本書分為十個章節，每個章節討論一個具體的案例研究。因此，本書以解決問題為導向。書中附有Python代碼，以Jupyter筆記本的形式幫助你獲得實踐經驗。本書的章節中一個反復出現的模式是幫助你對數據有一些直覺，然後實施和對比各種解決方案。

通過閱讀本書，你將能夠理解並應用Python中的各種技術，包括文本預處理、文本表示、降維、機器學習、語言建模、可視化和評估。

你將學到什麼：
- 理解文本的機器學習基本概念
- 發現如何表示文本數據並構建語言模型
- 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了解其優缺點
- 應用降維進行可視化和分類
- 整合和微調機器學習算法和模型
- 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

本書適合計算機科學、編程、數據科學、信息學、商業分析、統計、語言技術等領域的專業人士，他們希望在文本的機器學習領域進行輕鬆的職業轉變。相關學科的學生將從內容的實際方面以及理論的呈現方式中受益。最後，教授類似課程的教授將能夠選擇相關的內容和難度。開始閱讀本書需要初級水平的Python編程知識。