This course consists of ~25 tutorials and hands-on assignments to learn natural language processing. You can think of this course as a "Free Online Nano Book". You'll learn core natural language processing concepts, algorithms and applications (including code in Python). All tutorials are free. You need to upgrade to Pro to access quizzes and assignments.
Natural language processing comprises of a set of computational techniques to understand natural languages such as English, Spanish, Chinese, etc. NLP techniques are used for search engine ranking, automatic translation, filtering spam email, etc. Recent advanced applications of NLP techniques have also started to be used for question answering, auto-replying and in digital assistants like Amazon Alexa and Google Assistant.
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This section has just one tutorial, introducing you to NLP, discussing NLP applications, its challenges and various approaches to NLP.
We first introduce TF-IDF (term frequency, inverse document frequency), a very commonly used measure in NLP to weigh the importance of different words. This helps us in search engine ranking (also called document retrieval), finding similar or related documents, and so on.
Then we move on to a couple of tutorials on text classification (separating different types of documents into pre-defined categories) and topic modeling (organizing documents into groups when pre-defined categories are not available).
In this section, we will explore methods for understanding the syntax and structure of text. This sort of processing is important in-order to understand the meaning of sentences (in contrast to just predicting a category), and is used in applications like question answering and text summarization.
In the first tutorial, we'll see how to identify whether each individual word is a noun, pronoun, adjective, verb, etc. In the second, we'll see how to separate a sentence into noun phrases, verb phrases, and so on. Then, we'll see how to parse a sentence, which reveals not only the roles of individual words and phrases, but also the relation between them. Lastly, we'll see how to detect named entities in text, like people, places, companies, and so on.
Word Clouds are a great way of visualizing a document or a set of documents. You can see a word could for NLP at the top of this article.
Language Identification is the problem of identifying whether a given text is written in English, Spanish, French, etc. The last tutorial in this section describes an algorithm to perform automatic spelling correction.
Deep Learning is an advanced set of techniques in machine learning which has achieved state-of-art results in tasks like language translation, automatic replies, and more. In this section, we'll first introduce core concepts in deep learning (first 2 tutorials), and then discuss deep learning methods for NLP (last 3 tutorials). If you would like to learn deep learning more throughly, see: Learn Deep Learning: From Beginner to Expert.
