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1. |
Record Nr. |
UNINA9910824972803321 |
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Autore |
Lantz Brett |
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Titolo |
Machine learning with R / / Brett Lantz |
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Pubbl/distr/stampa |
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Birmingham : , : Packt Publishing, , 2013 |
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ISBN |
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1-68015-358-7 |
1-78216-215-1 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (396 p.) |
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Collana |
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Community experience distilled |
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Soggetti |
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Machine learning - Statistical methods |
R (Computer program language) |
Programming languages (Electronic computers) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or |
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those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data. |
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2. |
Record Nr. |
UNINA9910812890203321 |
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Autore |
Campesato Oswald |
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Titolo |
Natural Language Processing Fundamentals for Developers |
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Pubbl/distr/stampa |
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Bloomfield : , : Mercury Learning & Information, , 2021 |
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©2021 |
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ISBN |
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1-68392-655-2 |
1-68392-656-0 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (382 pages) |
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Disciplina |
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Soggetti |
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COMPUTERS / Natural Language Processing |
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Lingua di pubblicazione |
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Formato |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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NLPFD.Ch00.FM.pdf -- NLPFD.Ch1.pdf -- NLPFD.Ch2.pdf -- NLPFD.Ch3.pdf -- NLPFD.Ch4.pdf -- NLPFD.Ch5.pdf -- NLPFD.Ch6.pdf -- NLPFD.Ch7.pdf -- NLPFD.Ch8.AppA.pdf -- NLPFD.Ch9.AppB.pdf. |
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Sommario/riassunto |
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This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of |
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managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included.FEATURES:Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statisticsFeatures companion files with source code and figures from the book.The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com. |
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