NLP is not something all data scientists necessarily work with and are required to know. Whether or not you are, depends on the company interviewing you for a data science position. Well, you'll have to know what it is so you can avoid it in your career, if nothing else. In case you're intrigued by NLP and willing to learn more, you will benefit from knowing what interview questions you could expect. No, it's not that pseudoscientific psychological approach that gained popularity recently.

This article was published as a part of the Data Science Blogathon. Natural language processing (NLP) is the branch of computer science and, more specifically, the domain of artificial intelligence (AI) that focuses on providing computers the ability to understand written and spoken language in a way similar to that of humans. Combining computational linguistics (rule-based modeling of human language) with statistical, machine learning, and deep learning models is natural language processing (NLP). Together, these technologies enable computers to'understand' the whole meaning of human language in the form of text or speech data, including the speaker's or writer's purpose and emotion. NLP is the driving force behind computer systems that translate text from one language to another, respond to spoken commands, and swiftly summarise massive amounts of information--even in real-time.

It's one thing to practice NLP and another to crack interviews. Giving an interview for NLP role is very different from a generic data science profile. In just a few years, the questions have changed completely because of transfer learning and new language models. I have personally experienced that NLP interviews are getting tough with time as we make more progress. Earlier, it was all about SGD, naive-bayes and LSTM, but now its more about LAMB, transformer and BERT.