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Nikos Tsourakis
Machine Learning Techniques for Text
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1162122Machine Learning Techniques for Texthttps://www.gandhi.com.mx/machine-learning-techniques-for-text/phttps://gandhi.vtexassets.com/arquivos/ids/1087930/ba14d930-b8e3-47d0-bcf6-32f024357a9e.jpg?v=638337348893430000695772MXNPackt PublishingInStock/Ebooks/1154311Machine Learning Techniques for Text695772https://www.gandhi.com.mx/machine-learning-techniques-for-text/phttps://gandhi.vtexassets.com/arquivos/ids/1087930/ba14d930-b8e3-47d0-bcf6-32f024357a9e.jpg?v=638337348893430000InStockMXN99999DIEbook20229781803236292_W3siaWQiOiI5ZWJmMTZkNy03ZTkzLTQzZDgtYTIyZC02N2E1MjVlZjIwZTAiLCJsaXN0UHJpY2UiOjc3MiwiZGlzY291bnQiOjc3LCJzZWxsaW5nUHJpY2UiOjY5NSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781803236292_<p><strong>Soup up text processing with latest techniques in natural language processing in python.</strong></p><h4>Key Features</h4><ul><li>Learn how to extract word embeddings representation.</li><li>Learn how to use the Random Forest and the Decision Trees algorithms.</li><li>Learn how to apply curriculum and reinforcement learning.</li></ul><h4>Book Description</h4><p>Machine Learning and Python offer unique opportunities to exploit data that involve text and more specifically human language.</p><p>There is a rapid demand for professionals that can process text data and extract meaningful information out of it.</p><p>There is a plethora of textbooks that either present complicated theoretical concepts or focus disproportionately on Python code. This book steers an intermediate way and keeps the right balance between theory and practice based on various case studies.</p><p>A good metaphor on which this work builds upon is the relation between an experienced craftsperson and his/her trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action.</p><p>Each chapter follows the same pattern for presenting the material. At a high level, it consists of three steps that include: (1) acquire some intuition on the data (exploratory data analysis), (2) put in action a few machine learning algorithms (for training and inference), and (3) evaluate their performance on the problem under study (metrics).</p><p>By the end of this book, you will be able to apply a gamut of techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, visualization, and performance evaluation.</p><h4>What you will learn</h4><ul><li>Perform exploratory data analysis on text corpora.</li><li>Use text preprocessing techniques.</li><li>Know how text data can be represented.</li><li>Apply dimensionality reduction for visualization and classification.</li><li>Understand fundamental concepts of text ML.</li><li>Incorporate algorithms and models for text ML.</li><li>Evaluate the results of the text analysis.</li><li>Know the tools for obtaining and storing text data.</li></ul><h4>Who This Book Is For</h4><p>The target audience of this book are data science professionals, NLP Engineers, Machine Learning developers. Professionals like Data Scientists with good knowledge in programming that seek more information on the field, or want to do a gentle career shift in machine learning for text will find this book essential. Beginner level knowledge of python programming is needed to learn from this book.</p><h4>Table of Contents</h4><ol><li>Introduction to Machine Learning for Text</li><li>Spam Detection</li><li>Topic Classification</li><li>Sentiment analysis</li><li>Recommender Systems</li><li>Machine Translation</li><li>Social Media</li><li>Text Summarization</li><li>Text Clustering</li><li>Conversational Bots</li></ol>...(*_*)9781803236292_<p><b>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</b></p><h4>Key Features</h4><ul><li>Learn how to acquire and process textual data and visualize the key findings</li><li>Obtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffs</li><li>Implement models for solving real-world problems and evaluate their performance</li></ul><h4>Book Description</h4>With the ever-increasing demand for machine learning and programming professionals, its 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. Its 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, youll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.<h4>What you will learn</h4><ul><li>Understand fundamental concepts of machine learning for text</li><li>Discover how text data can be represented and build language models</li><li>Perform exploratory data analysis on text corpora</li><li>Use text preprocessing techniques and understand their trade-offs</li><li>Apply dimensionality reduction for visualization and classification</li><li>Incorporate and fine-tune algorithms and models for machine learning</li><li>Evaluate the performance of the implemented systems</li><li>Know the tools for retrieving text data and visualizing the machine learning workflow</li></ul><h4>Who this book is for</h4><p>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.</p>...9781803236292_Packt Publishinglibro_electonico_c911ebe2-a2d2-3926-9eab-efa60368ed11_9781803236292;9781803236292_9781803236292Nikos TsourakisInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-c2664900-a0cc-47a3-aecb-52636903b131.epub2022-10-31T00:00:00+00:00Packt Publishing