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Machine Learning Techniques for Text
Machine Learning Techniques for Text

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

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Machine Learning Techniques for Text

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 31, 2022
Length: 448 pages
Edition : 1st
Language : English
ISBN-13 : 9781803236292
Category :
Languages :

What do you get with eBook?

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Product Details

Publication date : Oct 31, 2022
Length: 448 pages
Edition : 1st
Language : English
ISBN-13 : 9781803236292
Category :
Languages :

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Table of Contents

12 Chapters
Chapter 1: Introducing Machine Learning for Text Chevron down icon Chevron up icon
Chapter 2: Detecting Spam Emails Chevron down icon Chevron up icon
Chapter 3: Classifying Topics of Newsgroup Posts Chevron down icon Chevron up icon
Chapter 4: Extracting Sentiments from Product Reviews Chevron down icon Chevron up icon
Chapter 5: Recommending Music Titles Chevron down icon Chevron up icon
Chapter 6: Teaching Machines to Translate Chevron down icon Chevron up icon
Chapter 7: Summarizing Wikipedia Articles Chevron down icon Chevron up icon
Chapter 8: Detecting Hateful and Offensive Language Chevron down icon Chevron up icon
Chapter 9: Generating Text in Chatbots Chevron down icon Chevron up icon
Chapter 10: Clustering Speech-to-Text Transcriptions Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(6 Ratings)
5 star 83.3%
4 star 16.7%
3 star 0%
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Revell B. Jan 13, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Machine Learning Techniques for Text is an in-depth guide to using modern machine learning techniques for text processing, dimensionality reduction, classification, and evaluation. The book is written for readers who are familiar with Python and have a basic understanding of machine-learning concepts. The author does a great job of introducing the reader to the different techniques used for text processing and how they can be applied using Python. The book covers a variety of techniques, from traditional methods like bag-of-words and TF-IDF to more advanced techniques such as word embeddings and deep learning. The explanations are clear and easy to follow, with plenty of code examples to help illustrate the concepts. The book also includes practical examples and case studies that demonstrate how the techniques can be applied in real-world scenarios. Overall, I would highly recommend this book to anyone interested in text processing and machine learning.
Amazon Verified review Amazon
Steven Fernandes Jan 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book explains machine learning for text, detecting spam emails, classifying topics of newsgroup posts, extracting sentiments from product reviews, and recommending music titles.It helps the user understand teaching machines to translate and summarize Wikipedia articles, detect hateful and offensive language, generate text in chatbots, and cluster speech-to-text transcriptions.The Python code given in the book is posted in the GitHub link.
Amazon Verified review Amazon
GK Dec 22, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a nice overview of methods for text processing and mining. It is written in a clear and engaging manner, containing many helpful figures and diagrams. It will feel accessible even to those who do not know much about these topics, and provides a nice overview of the various applications.
Amazon Verified review Amazon
Yiqiao Yin Dec 11, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Machine learning techniques for text involve the use of algorithms and statistical models to process and analyze natural language data. These techniques can be used for a variety of tasks, such as sentiment analysis, document classification, and language translation. Some common machine learning algorithms that are used for text data include support vector machines, decision trees, and neural networks. These algorithms can be trained on large amounts of labeled text data, and can then be used to make predictions or classify new, unseen text data.✅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
Amazon Verified review Amazon
Kyle Gallatin Jan 26, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book strikes a unique balance that many books in this genre struggle to achieve. It provides just enough math, theory and linguistics to give readers a peek "under the hood" of applied machine learning techniques for text, but always pulls away at the right moment with very aesthetically pleasing diagrams and easy-to-follow, hands-on code samples. As a result, this book is both a pleasure to read or use to get started in your latest NLP project.Structuring each chapter around a specific use case also helps frame the reader for the purpose of the topics they'll learn in each chapter and the theory behind their application. Those new to NLP will appreciate the slow build from tokenizing text to generating it with GPT2 - and those already familiar with the field will find an array of well-written examples and theoretically sound writing that help reinforce concepts you likely don't know as well as the author.Though the writing is a bit superfluous at times (I should know lol) this is one of the better-written books I've read as of late. Visual learners will love the diagrams, cognitive learners will love the text, and kinesthetic learners will love the examples.
Amazon Verified review Amazon
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