Natural Language Processing and Machine Learning for Developers / / Oswald Campesato.

• VerfasserIn: Campesato, Oswald,
• Place / Publishing House: Dulles, VA : : Mercury Learning and Information, , [2021]; ©2021
• Year of Publication: 2021
• Language: English
• Physical Description: 1 online resource (754 p.)
• Other title: Frontmatter --; Contents --; Preface --; Chapter 1 Introduction to NumPy --; Chapter 2 Introduction to Pandas --; Chapter 3 NLP Concepts (I) --; Chapter 4 NLP Concepts (II) --; Chapter 5 Algorithms and Toolkits (I) --; Chapter 6 Algorithms and Toolkits (II) --; Chapter 7 Introduction to Machine Learning --; Chapter 8 Classifiers in Machine Learning --; Chapter 9 NLP Applications --; Chapter 10 NLP and TF2/Keras --; Chapter 11 Transformer, BERT, and GPT --; Appendix A Data and Statistics --; Appendix B Introduction to Python --; Appendix C Introduction to Regular Expressions --; Appendix D Introduction to Keras --; Appendix E Introduction to TensorFlow 2 --; Appendix F Data Visualization --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781683926177
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Oswald Campesato.

This book is for developers who are looking for an introduction to basic concepts in NLP and machine learning. Numerous code samples and listings are included to support myriad topics. The first two chapters contain introductory material for NumPy and Pandas, followed by chapters on NLP concepts, algorithms and toolkits, machine learning, and NLP applications. The final chapters include examples of NLP tasks using TF2 and Keras, the Transformer architecture, BERT-based models, and the GPT family of models. The appendices contain introductory material (including Python code samples) for various topics, including data and statistics, Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn. Companion files with source code and figures are included. FEATURES:Covers extensive topics related to natural language processing and machine learningIncludes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and moreFeatures companion files with source code and color figures from the book.The companion files are available online by emailing the publisher with proof of purchase at [e-mail].