NLP is that one competence area in the field of data science which has gained wide recognition in the last couple of years. Considering how emerging of a technology NLP is and how rapidly it is gaining traction in the industry, it is quite obvious that the market is in need of those who excel in the same. This is where NLP courses come into play. Having said that, have a look at the top 10 online NLP courses beginners should attend in 2022.

Data Science: Natural language processing (NLP) in Python

This course from Udemy helps the student to build multiple practical systems using Natural language processing. Yet another point to note here is that this doesn’t contain any hard math. By the end of this course, you’ll be able to write your own spam detection code and sentiment analysis code in Python and perform latent semantic analysis as well.

Natural language processing (NLP) with BERT

“Natural language processing (NLP) with BERT” is a course on Udemy that throws light on how to perform semantic analysis. This NLP course is designed in such a manner that it allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks.

Introducing Text Analytics

Introducing Text Analytics is a 6-week long course available on edX where you will learn the core techniques of Natural language processing (NLP) and computational linguistics. Well, not just that – here, you will also get to learn how to use Python packages like pandas, scikit-learn, and TensorFlow, among others.

Natural language processing Specialization

This Coursera course will enable you to learn using logistic regression, word vectors, etc. to implement sentiment analysis, complete analogies & translate words. You will also learn how to create tools to translate languages and summarize text, and even build chatbots.

Hands On Natural language processing (NLP) using Python

As the name suggests, this NLP course from Udemy will help you have a sound understanding of the various concepts of Natural language processing along with their implementation. Here, you will not just be able to build Natural language processing-based applications but also learn about the different modules available in Python for NLP in detail.

Text Analytics 2: Visualizing Natural language processing

This 6-week long edX course is a practical approach to visualize and interpret the output of text analytics. Here, you’ll be able to learn how to create visualizations ranging from word clouds, heatmaps, and line plots to distribution plots, choropleth maps, and facet grids.

NLP – Natural language processing with Python

“NLP – Natural language processing with Python” is a Udemy course that is exclusively designed to enable the user to learn how to use Natural language processing with the Python programming language. NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization, etc. are all covered in this NLP course.

Natural language processing with Probabilistic Models

“Natural language processing with Probabilistic Models” is offered by Coursera with an aim of designing NLP applications that perform question-answering and sentiment analysis, creating tools to translate languages, and summarizing text. Well, not just that – the student can even build a chatbot by the end of this course. Can it get any better?

Natural language processing

Natural language processing – a Coursera course is aimed at covering a wide range of tasks in Natural language processing right from the basics to advanced. Be it sentiment analysis, summarization, or dialogue state tracking, this NLP course has got you covered. On completion, you will be in a position to recognize NLP tasks in your day-to-day work.

Deep Learning and NLP A-Z™: How to create a ChatBot

Someone who is keen on wanting to start a career in Data Science can enrol for this course. Evidently, this NLP course is aimed at creating a chatbot. Here, you’ll not just learn the theoretical aspects of NLP but also how to implement state-of-the-art Deep Natural language processing models in Tensorflow and Python.