What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Transformers for Natural Language Processing

€67.99

€35.99

Machine Learning with PyTorch and Scikit-Learn

€41.99

Total € 145.97

FAQs

How do I buy and download an eBook?

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

You may make copies of your eBook for your own use onto any machine
You may not pass copies of the eBook on to anyone else

How can I make a purchase on your website?

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

Register on our website using your email address and the password.
Search for the title by name or ISBN using the search option.
Select the title you want to purchase.
Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title.
Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)

Where can I access support around an eBook?

If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
To view your account details or to download a new copy of the book go to www.packtpub.com/account
To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us

What eBook formats do Packt support?

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks?

You can get the information you need immediately
You can easily take them with you on a laptop
You can download them an unlimited number of times
You can print them out
They are copy-paste enabled
They are searchable
There is no password protection
They are lower price than print
They save resources and space

What is an eBook?

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.

Filter reviews by

All

Packt verified reviews

Amazon verified reviews

Revell B. Jan 13, 2023

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

Steven Fernandes Jan 08, 2023

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.

GK Dec 22, 2022

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.

Yiqiao Yin Dec 11, 2022

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

Kyle Gallatin Jan 26, 2023

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.

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

What do you get with eBook?