Summary

In this chapter, we introduced you to the field of NLP, which is a subfield of AI. The chapter highlights the importance of mathematical foundations, such as linear algebra, statistics and probability, and optimization theory, which are necessary to understand the algorithms used in NLP. It also covers the challenges faced in NLP, such as understanding the context and meaning of words, the relationships between them, and the need for labeled data. We discussed the recent advancements in NLP, including pre-trained language models, such as BERT and GPT, and the availability of large amounts of text data, which has led to improved performance in NLP tasks. We touched on the importance of text preprocessing as you gains knowledge of the importance of data cleaning, data normalization, stemming, and lemmatization in text preprocessing. We then talked about how the coming together of NLP and ML is driving advancements in the field and is becoming an increasingly important tool for automating tasks and improving human-computer interaction.

After learning from this chapter, you will be able to understand the importance of NLP, ML, and DL techniques. you will be able to understand the recent advancements in NLP, including pre-trained language models. you will also have gained knowledge of the importance of text preprocessing and how it plays a crucial role in data preparation for NLP tasks and in data cleaning.

In the next chapter, we will cover the mathematical foundations of ML. These foundations will serve us throughout the book.