Text Analysis With Python A Research Oriented Guide
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Text Analysis With Python
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Author : Mamta Mittal; Gopi
language : en
Publisher:
Release Date : 2022-08-12
Text Analysis With Python written by Mamta Mittal; Gopi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-12 with categories.
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.
Text Analysis With Python A Research Oriented Guide
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Author : Mamta Mittal
language : en
Publisher: Bentham Science Publishers
Release Date : 2022-08-12
Text Analysis With Python A Research Oriented Guide written by Mamta Mittal and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-12 with Computers categories.
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.
Deep Learning
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Author : Dr. Venkata Kiran Kumar Ravi, RaviKumar Bhuvanagiri, Dr. A. Ajmal Khaan, Mrs. S. Jagadeeswari
language : en
Publisher: BR Publications
Release Date : 2025-12-31
Deep Learning written by Dr. Venkata Kiran Kumar Ravi, RaviKumar Bhuvanagiri, Dr. A. Ajmal Khaan, Mrs. S. Jagadeeswari and has been published by BR Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-31 with Computers categories.
Deep learning is a subfield of artificial intelligence that focuses on training multi-layered neural networks to learn patterns from large datasets. It enables machines to automatically extract features from raw data, reducing the need for manual feature engineering. Deep learning models consist of multiple hidden layers that progressively transform inputs into meaningful representations. Techniques such as backpropagation and gradient descent are used to optimize network parameters. Activation functions introduce nonlinearity, allowing models to solve complex problems. Convolutional neural networks handle image data, while recurrent and transformer models process sequential information. Large-scale data and powerful computing resources are essential for effective training. Regularization methods help prevent overfitting and improve generalization. Transfer learning allows reuse of pretrained models for new tasks. Deep learning powers applications like speech recognition, computer vision, and natural language processing. Model performance depends on architecture design and data quality. Ethical considerations and interpretability are increasingly important. Overall, deep learning drives modern intelligent systems and technological innovation.
The Practical Guide To Hr Analytics
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Author : Shonna D. Waters
language : en
Publisher: Kogan Page Publishers
Release Date : 2018-06-15
The Practical Guide To Hr Analytics written by Shonna D. Waters and has been published by Kogan Page Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-15 with Computers categories.
HR professionals are increasingly expected to understand and apply data analytics, but many don't know where to start. The Practical Guide to HR Analytics offers a clear, practical guide to understanding and applying data to real-world HR issues. From making the business case for analytics to launching an HR analytics function, the book walks readers through the forms, uses, and interpretations of data in an HR context. It covers essential topics like avoiding common pitfalls, visualizing data effectively and using storytelling to communicate findings. With straightforward language and actionable advice, it helps demystify analytics for professionals at all levels. Whether you're new to analytics or seeking to sharpen your skills, this is your go-to resource for data-driven HR.
Text Analytics With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2019-05-21
Text Analytics With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-21 with Computers categories.
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
Natural Language Processing And Computational Linguistics
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Author : Bhargav Srinivasa-Desikan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-29
Natural Language Processing And Computational Linguistics written by Bhargav Srinivasa-Desikan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-29 with Computers categories.
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
The Library Journal
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Author :
language : en
Publisher:
Release Date : 2000
The Library Journal written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Libraries categories.
Includes, beginning Sept. 15, 1954 (and on the 15th of each month, Sept.-May) a special section: School library journal, ISSN 0000-0035, (called Junior libraries, 1954-May 1961). Also issued separately.
Library Journal
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Author :
language : en
Publisher:
Release Date : 2000
Library Journal written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Libraries categories.
Subject Guide To Books In Print
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Author :
language : en
Publisher:
Release Date : 1991
Subject Guide To Books In Print written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with American literature categories.
Mobile Forensic Investigations A Guide To Evidence Collection Analysis And Presentation
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Author : Lee Reiber
language : en
Publisher: McGraw Hill Professional
Release Date : 2015-11-22
Mobile Forensic Investigations A Guide To Evidence Collection Analysis And Presentation written by Lee Reiber and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-22 with Computers categories.
This in-depth guide reveals the art of mobile forensics investigation with comprehensive coverage of the entire mobile forensics investigation lifecycle, from evidence collection through advanced data analysis to reporting and presenting findings. Mobile Forensics Investigation: A Guide to Evidence Collection, Analysis, and Presentation leads examiners through the mobile forensics investigation process, from isolation and seizure of devices, to evidence extraction and analysis, and finally through the process of documenting and presenting findings. This book gives you not only the knowledge of how to use mobile forensics tools but also the understanding of how and what these tools are doing, enabling you to present your findings and your processes in a court of law. This holistic approach to mobile forensics, featuring the technical alongside the legal aspects of the investigation process, sets this book apart from the competition. This timely guide is a much-needed resource in today’s mobile computing landscape. Notes offer personal insights from the author's years in law enforcement Tips highlight useful mobile forensics software applications, including open source applications that anyone can use free of charge Case studies document actual cases taken from submissions to the author's podcast series Photographs demonstrate proper legal protocols, including seizure and storage of devices, and screenshots showcase mobile forensics software at work Provides you with a holistic understanding of mobile forensics