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 (available for downloading with Amazon proof of purchase by writing to info@merclearning.com).

Features

+Covers extensive topics related to natural language processing and machine learning

+Includes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and more

+Features companion files with source code and color figures from the book (available for downloading with Amazon proof of purchase by writing to info@merclearning.com).

Brief Table of Contents

1: Introduction to NumPy. 2: Introduction to Pandas. 3: NLP Concepts (I). 4: NLP Concepts (II). 5. Algorithms and Toolkits (I). 6. Algorithms and Toolkits (II). 7: Introduction to Machine Learning. 8: Classifiers in Machine Learning.

9: NLP Applications. 10: NLP and TF2 / Keras. 11: Transformer, BERT, and GPT. Appendices. A: Data and Statistics. B: Introduction to Python. C: Introduction to Regular Expressions. D: Introduction to Keras. E: Introduction to TF2.

F: Data Visualization. Index.

About the Author

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, NLP, Android, and TensorFlow. He is the author/co-author of over twenty-five books including  TensorFlow 2 Pocket Primer,  Python 3 for Machine Learning, and the  Python Pocket Primer (Mercury Learning).

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