What are the most important concepts in the domain of an NLP engineer?

  • Natural Language Processing is currently one of AI’s trendiest fields.
  • Despite the fact that many of the concepts are difficult to grasp, these foundations should help you get started in the subject and prepare you for more advanced courses:
  • Text Processing Fundamentals - Learning the most significant methods for text processing is one of the most important concepts to deal with in programming languages. Knowing how to alter text back and forth, employing regular expressions, and slicing strings are just a few of the most essential instances of dealing with strings in a computer language.
  • Natural Language Toolkit Library - The Natural Language Toolkit Library is one of the most well-known Natural Language Processing libraries. It is one of the most useful sites for understanding some of the principles of NLP.
  • Text Data Reading - In the last decade, the massive amount of text data traveling on the internet has expanded tremendously. Aside from gathering data from the internet, NLP practitioners (like most data scientists) must deal with a variety of files in various forms.
  • Vectors of Words (and Neural Networks) - Word vectors are one of the most essential strategies in NLP today, and they’re also very helpful in understanding how Artificial Neural Networks are employed in NLP.
  • Recurrent Neural Networks (RNNs) - Text generation is another area of Natural Language Processing that has seen significant advancements because of the use of Neural Networks.
  • Text classification - It uses prediction models to classify text into categories. Some of the most commonly used methods that categorize text into specific buckets are tree-based models, Naive Bayes Classifiers, and Neural Networks.