Download or read book Natural Language Processing with Python written by Steven Bird and published by "O'Reilly Media, Inc.". This book was released on 2009-06-12 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

Download or read book Natural Language Processing Python and NLTK written by Nitin Hardeniya and published by Packt Publishing Ltd. This book was released on 2016-11-22 with total page 687 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book Break text down into its component parts for spelling correction, feature extraction, and phrase transformation Work through NLP concepts with simple and easy-to-follow programming recipes Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn The scope of natural language complexity and how they are processed by machines Clean and wrangle text using tokenization and chunking to help you process data better Tokenize text into sentences and sentences into words Classify text and perform sentiment analysis Implement string matching algorithms and normalization techniques Understand and implement the concepts of information retrieval and text summarization Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: NTLK essentials by Nitin Hardeniya Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.

Download or read book NLTK written by Posts & Telecom Press and published by Packt Publishing Ltd. This book was released on 2024-05-23 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: NLTK和Python库构建机器学习应用 Key Features 通过NLTK库与一些Python库的结合来实现复杂的NLP任务和机器学习应用 探讨一些与数据科学相关的任务,通过这些任务来学习如何从零开始构建自定义的标识器和解析器 深度探索NLP领域的基本概念,为各种开源的Python工具和库提供具有实践意义的见解 Book Description自然语言处理(NLP)属于人工智能与计算机语言学的交叉领域,处理的是计算机与人类语言之间的交互问题。随着人机交互需求的日益增长,计算机具备处理当前主要自然语言的能力已经成为了一个必然趋势。NLTK正是这一领域中一个强大而稳健的工具包。 在这本书中,我们首先会介绍一些与NLP相关的知识。然后,我们会探讨一些与数据科学相关的任务,通过这些任务来学习如何从零开始构建自定义的标识器和解析器。在此过程中,我们将会深度探索NLP领域的基本概念,为这一领域各种开源的Python工具和库提供具有实践意义的见解。接下来,我们将会介绍如何分析社交媒体网站,发现热门话题,进行舆情分析。最后,我们还会介绍一些用于处理大规模文本的工具。 在阅读完本书之后,您将会对NLP与数据科学领域中的概念有一个充分的了解,并能将这些知识应用到日常工作中。如果您是NLP或机器学习相关领域的爱好者,并有一些文本处理的经验,那么本书就是为你量身定做的。此外,这本书也是专业Python程序员快速学习NLTK库的理想选择。What you will learn 了解自然语言的复杂性以及机器对它们的处理方式。 如何利用标识化处理手段清理文本歧义,并利用分块操作更好地处理数据。 探索不同标签类型的作用,并学习如何将句子标签化。 如何根据自己的需要来创建自定义的解析器和标识器。 如何构建出具有拼写检查、搜索、机器翻译以及问答系统等功能的实用程序。 如何通过信息爬取与捕获的手段对相关数据内容进行检索。 如何通过特性的提取与选取,构建出针对不同文本的分类系统。 如何使用各种第三方Python库,如pandas、scikit-learn、matplotlib、gensim。 如何对社交媒体网站进行分析,包括发掘热门话题、舆情分析等。 Who this book is for 自然语言处理方面的开发人员、Python开发爱好者、机器学习领域的研究人员及开发者

Download or read book Python Data Science Essentials written by Alberto Boschetti and published by Packt Publishing Ltd. This book was released on 2018-09-28 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Download or read book Data Science Essentials in Python written by Dmitry Zinoviev and published by Pragmatic Bookshelf. This book was released on 2016-08-10 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Download or read book Python Data Science Essentials written by Alberto Boschetti and published by Packt Publishing Ltd. This book was released on 2016-10-28 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Download or read book Data Science and Artificial Intelligence written by Chutiporn Anutariya and published by Springer Nature. This book was released on 2023-11-17 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the First International Conference, DSAI 2023, held in Bangkok, Thailand, during November 27–30, 2023. The 22 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions. This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and artificial intelligence.

Download or read book A Hands on Introduction to Big Data Analytics written by Funmi Obembe and published by SAGE Publications Limited. This book was released on 2024-02-23 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional. It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark. Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning. Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton. Dr Ofer Engel is a Data Scientist at the University of Groningen.

Download or read book NLTK Essentials written by Nitin Hardeniya and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are an NLP or machine learning enthusiast with some or no experience in text processing, then this book is for you. This book is also ideal for expert Python programmers who want to learn NLTK quickly.

Download or read book A Hands On Introduction to Essential Python Libraries and Frameworks With Code Samples written by Murat Durmus and published by Murat Durmus. This book was released on 2023-03-02 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Python libraries and frameworks that every aspiring data scientist, ML engineer, and Python developer should know. "Python is not just a language, it's a community where developers can learn, collaborate and create wonders." ~ Guido van Rossum (Creator of Python)

Download or read book Intelligent Interactive Multimedia Systems and Services 2017 written by Giuseppe De Pietro and published by Springer. This book was released on 2017-05-26 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Tenth International KES Conference on Intelligent Interactive Multimedia Systems and Services: IIMSS-17. It includes 57 full papers organized into topical sections, ranging from visual data processing to big data analytics, and from multimedia to intelligent and cognitive systems. The conference took place as part of the Smart Digital Futures 2017 multi-theme conference, held in Vilamoura, Algarve, Portugal on 21–23 June 2017, which brings together AMSTA, IDT, InHorizons, InMed, SEEL and IIMSS in one venue. It provided an international forum for researchers and scientists to share their work and experiences in the field of multimedia and intelligent interactive systems and services.

Download or read book Artificial Intelligence Doctoral Symposium written by Habiba Drias and published by Springer Nature. This book was released on 2023-08-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes selected papers presented during the 5th Artificial Intelligence Doctoral Symposium, AID 2022, held in Algiers, Algeria, in September 2022. The 22 presented full papers were thoroughly reviewed and selected from the 38 qualified submissions. They are organized in the following topical sections: data mining; metaheuristics and swarm intelligence; computer vision; Artificial Intelligence applications; machine and deep learning; NLP and text mining.

Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Download or read book Advanced Computing Strategies for Engineering written by Ian F. C. Smith and published by Springer. This book was released on 2018-06-09 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This double volume set ( LNAI 10863-10864) constitutes the refereed proceedings of the 25th International Workshop, EG-ICE 2018, held in Lausanne, Switzerland, in June 2018. The 58 papers presented in this volume were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on Advanced Computing in Engineering, Computer Supported Construction Management, Life-Cycle Design Support, Monitoring and Control Algorithms in Engineering, and BIM and Engineering Ontologies.

Download or read book Handbook of Research on Applied AI for International Business and Marketing Applications written by Christiansen, Bryan and published by IGI Global. This book was released on 2020-09-25 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students.

Download or read book Secure Knowledge Management In The Artificial Intelligence Era written by Ram Krishnan and published by Springer Nature. This book was released on 2022-02-22 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference On Secure Knowledge Management In Artificial Intelligence Era, SKM 2021, held in San Antonio, TX, USA, in 2021. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 30 submissions. They were organized according to the following topical sections: ​intrusion and malware detection; secure knowledge management; deep learning for security; web and social network.

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity