Thursday, January 20, 2022

How to become an NLP Expert by self-learning?

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Natural Language Processing (NLP) is a fascinating topic in AI, and it has already spawned innovations such as chatbots, voice assistants, translators, and a slew of other daily utilities.

There are hundreds of educational resources for natural language processing online, just like there are for every other data science discipline. Furthermore, like every discipline, most of these resources focus on specific field principles instead of the overall picture. These lessons are necessary if you know what you'd like to learn and in what sequence you want to study it. The issue comes when you're new to the field and don't know where to begin to achieve your goal.

When you want to learn anything new, the first reasonable step is to search for the best courses for AI and Machine Learning or a PG program in AI-ML to pursue and collect all the information you want in the most efficient manner possible. However, finding a straightforward, accurate, and practical plan is often challenging. It is a learning adventure in and of itself that must be navigated efficiently.

Compared to the number of in-depth lessons, there aren't many generic ones with a broader scope and stages that may be followed. This article will help you better understand the procedures you must take, the resources you must study, and the information you must acquire to be deemed a natural language processing expert.

For this article to be comprehensive, there are a few elements that you must address to understand natural language processing:

  • Programming

Programming is a necessary ability for anybody interested in pursuing a career in the technological industry. Coding is one of those abilities that is simple to master, yet it soon becomes complicated and is a critical artificial intelligence course eligibility criterion. You must have at least a rudimentary understanding of coding in any computer language to use it to create any Natural Language Processing (NLP) application.

  • Math, Statistics, and Probability

Natural language processing, like any other data science, relies heavily on math and statistics. You must be acquainted with four key parts of arithmetic and statistics to comprehend natural language processing algorithms - linear algebra, probability theory, calculus, and statistics. People with mathematics and statistics backgrounds have a relative advantage over people who don’t. Math, Statistics, and Probability are the foundations of data science; one cannot skip this step. Proficiency in these areas plays a significant role in distinguishing a good Data Scientist from a bad one.

  • Text Preprocessing

Text preprocessing is the initial stage in every natural language processing project. Text processing is a broad term that refers to a range of methods that may be applied to a text to prepare it for further evaluation. For example, text preprocessing includes stemming, lemmatization, tokenization, and lexicons, to name a few. These crucial text preparation techniques are straightforward and are frequently pre-implemented in most Python or natural language processing (NLP) tools.

  • Machine Learning Basics

The majority of data science tasks require machine learning. Understanding the fundamentals of machine learning before diving into natural language processing will make a significant impact. Multiple machine learning ideas, libraries, and techniques are used in most Natural Language Processing methods. You must be acquainted with the many types of machine learning algorithms, including supervised, unsupervised, and transferred learning, to completely comprehend these algorithms and use them in your NLP application accordingly to get optimized results.

  • NLP Core Techniques

Text preparation methods are used to clear up and prepare the text for fundamental NLP approaches to examine. These methods are designed to do specific tasks and extract data from text. Using word vectors, categorizing text, creating sequence models, parsing text to derive syntactic representation, and performing certain machine translations are all examples of NLP. Performing word embeddings on the text by matching text to integer vector numbers to make machine learning techniques easier to apply is one step that might assist make this process easier.


Starting up with a new skill is usually tricky, but it may be made a little simpler if you have a real and concise path to follow to achieve your objectives. The stages in this post are designed to be comprehensive. If you've completed a few steps already, you can move to the next.

You'll know enough to work on projects and apply for employment. However, Natural Language Processing is constantly evolving, with optimization schemes and approaches being introduced daily. As a Natural Language Processing specialist, you'll need to stay updated on both the technological and scholarly advancements in the field.


  • Where to find NLP datasets?

Ans. Kaggle has numerous free datasets to choose from. 

  • What are the best resources for learning about Natural Language Processing?

Ans. If you're serious about learning NLP, it is suggested to start with the basics by reading Jurafsky and Martin's Speech and Language Processing. The third edition is currently being written, and certain chapters are accessible in PDF format. Furthermore, read Yoav Goldberg's introduction to understanding Deep Learning for NLP.

  • What are some areas in NLP?

Ans. Natural Language Processing can be used for

  1. Semantic Analysis
  2. Automatic summarization
  3. Text classification
  4. Question Answering.

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