Natural Language Processing and Machine Learning for Developers (eBook, ePUB)
A Practical Guide to Advanced Techniques and Applications of NLP
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This book introduces developers to basic concepts in NLP and machine learning, providing numerous code samples to support the topics covered. The journey begins with introductory material on NumPy and Pandas, essential for data manipulation. Following this, chapters delve into NLP concepts, algorithms, and toolkits, providing a solid foundation in natural language processing. As you progress, the book covers machine learning fundamentals and classifiers, demonstrating how these techniques are applied in NLP. Practical examples using TF2 and Keras illustrate how to implement various NLP tasks. ...
This book introduces developers to basic concepts in NLP and machine learning, providing numerous code samples to support the topics covered. The journey begins with introductory material on NumPy and Pandas, essential for data manipulation. Following this, chapters delve into NLP concepts, algorithms, and toolkits, providing a solid foundation in natural language processing.
As you progress, the book covers machine learning fundamentals and classifiers, demonstrating how these techniques are applied in NLP. Practical examples using TF2 and Keras illustrate how to implement various NLP tasks. Advanced topics include the Transformer architecture, BERT-based models, and the GPT family of models, showcasing the latest advancements in the field.
The final chapters and appendices offer a comprehensive overview of related topics, including data and statistics, Python3, regular expressions, and data visualization with Matplotlib and Seaborn. Companion files with source code and figures ensure a hands-on learning experience. This book equips you with the knowledge and tools needed to excel in NLP and machine learning.
As you progress, the book covers machine learning fundamentals and classifiers, demonstrating how these techniques are applied in NLP. Practical examples using TF2 and Keras illustrate how to implement various NLP tasks. Advanced topics include the Transformer architecture, BERT-based models, and the GPT family of models, showcasing the latest advancements in the field.
The final chapters and appendices offer a comprehensive overview of related topics, including data and statistics, Python3, regular expressions, and data visualization with Matplotlib and Seaborn. Companion files with source code and figures ensure a hands-on learning experience. This book equips you with the knowledge and tools needed to excel in NLP and machine learning.
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