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  • 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: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

by Nikos Tsourakis (Author) Format: Kindle Edition
4.8 out of 5 stars (9)

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.

Table of Contents

  1. Introducing Machine Learning for Text
  2. Detecting Spam Emails
  3. Classifying Topics of Newsgroup Posts
  4. Extracting Sentiments from Product Reviews
  5. Recommending Music Titles
  6. Teaching Machines to Translate
  7. Summarizing Wikipedia Articles
  8. Detecting Hateful and Offensive Language
  9. Generating Text in Chatbots
  10. Clustering Speech-to-Text Transcriptions
Read more
Previous slide of product details
  1. ISBN-13
    978-1803236292
  2. Edition
    1st
  3. Publisher
    Packt Publishing
  4. Accessibility
    Learn more
  5. Publication date
    October 31, 2022
  6. Language
    English
  7. File size
    29.2 MB
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From the brand

Editorial Reviews

Review

“The book presents a perfect amalgamation of NLP and machine learning techniques for developing practical applications dealing with unstructured text data. Each chapter of this book is carefully designed around practical applications. Despite so much content on NLP and ML available online, this book would be my first choice for learning or revising any concept involving both NLP and ML.”

--

Aditya Bhattacharya, Explainable AI Researcher, KU Leuven



“As AI professionals, we often get lost in the latest LLM architectures, forgetting that text data is the base of human communication. This book explains foundational NLP concepts clearly and thoroughly, which are essential for understanding how LLMs solve real-world business issues.

Why I liked this book:

  • Step-by-step approach with clear illustrations and examples (in Python)
  • Explains the utility of each technique and method, presents practical use cases, and then delves into implementation, contrasting various solutions
  • Solution-oriented with case studies, many of which have high business relevancy

Key takeaways:

  • Fundamental concepts of ML for text
  • Text data representation and embeddings
  • Foundational concepts behind LLMs like transformers and attention mechanisms
  • Text preprocessing techniques and their trade-offs
  • Dimensionality reduction for visualization and classification
  • Fine-tuning algorithms and models for textual ML
  • Evaluating the performance of implemented systems
  • Tools for retrieving text data and visualizing the ML workflow

Who is it for?

Practitioners and managers of data analytics, data science, and data engineering, and students and professors in relevant disciplines. A beginner-level knowledge of Python programming is needed.

NLP and ML for text are pivotal in today's AI-driven world, especially with the great attention given to GenAI and LLMs these days. This book does not just chase the latest sexy trends but enables readers to understand and master the foundational concepts. Highly recommended reading.”

--

Andrea De Mauro, Executive Data & AI Advisor, Author of "Data Analytics Made Easy", Former Data Executive at Vodafone and P&G

About the Author

Nikos Tsourakis is a professor of computer science and business analytics at the International Institute in Geneva, Switzerland, and a research associate at the University of Geneva. He has over 20 years of experience designing, building, and evaluating intelligent systems using speech and language technologies. He has also co-authored over 50 research publications in the area. In the past, he worked as a software engineer, developing products for major telecommunication vendors. He also served as an expert for the European Commission and is currently a certified educator at the Amazon Web Services Academy. He holds a degree in electronic and computer engineering, a master's in management, and a PhD in multilingual information processing.

Product details

  • ASIN ‏ : ‎ B0BDSMY5WT
  • Publisher ‏ : ‎ Packt Publishing
  • Accessibility ‏ : ‎ Learn more
  • Publication date ‏ : ‎ October 31, 2022
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 29.2 MB
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 448 pages
  • ISBN-13 ‏ : ‎ 978-1803236292
  • Page Flip ‏ : ‎ Enabled
  • Best Sellers Rank: #1,396 in Data Modeling & Design (Books)
  • Customer Reviews:
    4.8 out of 5 stars (9)

About the author

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Nikos Tsourakis

Nikos Tsourakis

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Nikos Tsourakis is a professor of computer science and business analytics at the International Institute in Geneva, Switzerland, and a research associate at the University of Geneva. He has over 20 years of experience designing, building, and evaluating intelligent systems using speech and language technologies. He has also co-authored over 50 research publications in the area. In the past, he worked as a software engineer, developing products for major telecommunication vendors. He also served as an expert for the European Commission and is currently a certified educator at the Amazon Web Services Academy. He holds a degree in electronic and computer engineering, a master’s in management, and a PhD in multilingual information processing.

Customer reviews

4.8 out of 5 stars
9 global ratings
Modern ways to use machine-learning to conduct text processing
5 out of 5 stars
Modern ways to use machine-learning to conduct text processing
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.
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Top reviews from the United States

  • Kyle Gallatin
    5 out of 5 stars
    Fanstastic balance between understanding and application!
    Reviewed in the United States on January 26, 2023
    Format: Kindle
    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    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.

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  • Revell B.
    5 out of 5 stars
    Modern ways to use machine-learning to conduct text processing
    Reviewed in the United States on January 13, 2023
    Format: Paperback
    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    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.

    Modern ways to use machine-learning to conduct text processing
    Revell B.
    5 out of 5 stars
    Modern ways to use machine-learning to conduct text processing
    Reviewed in the United States on January 13, 2023
    Format: Paperback

    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.

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  • Yiqiao Yin
    5 out of 5 stars
    Highly recommend it
    Reviewed in the United States on December 11, 2022
    Format: Kindle
    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    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

    One person found this helpful
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  • Steven Fernandes
    5 out of 5 stars
    Fantastic book on machine learning on techniques for text
    Reviewed in the United States on January 8, 2023
    Format: Paperback
    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    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.

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  • Om S
    4 out of 5 stars
    "A Comprehensive Guide to Machine Learning Techniques for Text Data"
    Reviewed in the United States on February 10, 2023
    Format: Kindle
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    Full content visible, double tap to read brief content.

    "Machine Learning Techniques for Text" is a comprehensive guide to using machine learning for text data. The book covers the following topics in detail:

    Introducing Machine Learning for Text: This section provides a clear introduction to the field of machine learning and its application to text data.

    Detecting Spam Emails: This section explains how machine learning can be used to identify spam emails, by training a model to recognize patterns in the content of emails.

    Classifying Topics of Newsgroup Posts: The book explores how machine learning algorithms can be used to classify newsgroup posts into different topics based on their content.

    Extracting Sentiments from Product Reviews: This section explains how machine learning can be used to extract sentiments from product reviews, by training a model to recognize patterns in the language used in reviews.

    Recommending Music Titles: The book demonstrates how machine learning can be used to recommend music titles to users based on their listening history and preferences.

    Teaching Machines to Translate: This section explains how machine learning can be used to translate text from one language to another, by training a model to recognize patterns in the language used in the source and target languages.

    Summarizing Wikipedia Articles: The book explains how machine learning can be used to summarize long articles, by training a model to identify the most important sentences in the article.

    Detecting Hateful and Offensive Language: This section explores how machine learning can be used to detect hateful and offensive language, by training a model to recognize patterns in the language used in such messages.

    Generating Text in Chatbots: The book demonstrates how machine learning can be used to generate text in chatbots, by training a model to recognize patterns in the language used in human-generated messages.

    Clustering Speech-to-Text Transcriptions: The book explains how machine learning can be used to cluster speech-to-text transcriptions, by training a model to recognize patterns in the language used in speech.

    Overall, "Machine Learning Techniques for Text" is an excellent resource for anyone interested in using machine learning for text data. The book is well-written, with clear explanations and numerous examples, making it accessible to readers with a variety of backgrounds and levels of experience.

    One person found this helpful
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