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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)
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Summary

In this chapter, we learned about various concepts related to machine learning, starting with data exploration and preprocessing techniques. We then explored various machine learning models, such as logistic regression, decision trees, support vector machines, and random forests, along with their strengths and weaknesses. We also discussed the importance of splitting data into training and test sets, as well as techniques for handling imbalanced datasets.

The chapter also covered the concepts of model bias, variance, underfitting, and overfitting, and how to diagnose and address these issues. We also explored ensemble methods such as bagging, boosting, and stacking, which can improve model performance by combining the predictions of multiple models.

Finally, we learned about the limitations and challenges of machine learning, including the need for large amounts of high-quality data, the risk of bias and unfairness, and the difficulty of interpreting complex models. Despite...

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