Someone once told me, If you want to learn about something, start teaching it to others. While I am not good at 1:1 teaching or creating YouTube videos, I chose to blog as my medium of teaching. Around the start of the year 2022, I made a switch from a full-time front-end engineer to a Natural Language Processing (NLP) Engineer in my current organization. I started learning more about the NLP technologies that were being widely used. I got introduced to Stanford NLP, BERT, and related technologies. While I was getting comfortable with all of these, Chatgpt started to gain traction, and everyone was talking about it, and that is when I got introduced to terms like transformer learning, self-supervised learning, etc. It was when I decided to re-learn the concepts of that I started this series. Natural Language Processing (NLP) I will start with the basics of NLP and take you through my learnings along the way in the upcoming months. So let's get going. Natural Language Processing (NLP) Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on the interaction between computers and human language. It is an interdisciplinary field that combines linguistics, computer science, and information engineering to understand, interpret, and generate human language. Why use NLP One of the main goals of NLP is to enable computers to understand and analyze human language in a way that is similar to how humans do it. This allows computers to perform tasks such as , text summarization, sentiment analysis, and more. language translation Some of the most common NLP techniques include: breaking down the text into individual words or phrases Tokenization: reducing words to their base form Lemmatization: identifying the role of each word in a sentence (e.g. noun, verb, adjective) Part-of-speech tagging: identifying and classifying proper nouns in the text (e.g. people, places, organizations) Named Entity Recognition: determining the emotional tone of text (e.g. positive, negative, neutral) Sentiment Analysis: Usage of NLP NLP is used in a wide range of applications, such as chatbots, virtual assistants, language translation, and text summarization. In recent years, NLP has become increasingly important in industries such as finance, healthcare, and customer service. NLP is a rapidly growing field, and new techniques and technologies are constantly being developed to improve the accuracy and functionality of NLP systems. With the increasing amount of data being generated and the growing importance of understanding and analyzing human language, NLP is set to play an increasingly important role in the future of technology. In conclusion, NLP is a powerful tool that can be used to analyze and understand human language. From chatbots to text summarization, NLP is being used in a wide range of applications and industries. With the rapid pace of development in the field, it is an exciting time for NLP and its potential to revolutionize the way we interact with technology. That's all for now, folks. In the next blog in this series, I will cover more details on the NLP techniques I have listed above. Feel free to comment on how you like my blog or shoot me an email at If you have any queries, and I will try to answer. connect@nandan.dev You can also visit my website to read some of the articles at https://nandan.dev Stay tuned & connect with me on my social media channels. Make sure to subscribe to my newsletter to get regular updates on my upcoming posts. | | | Twitter Instagram Github Website Also published . here

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