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1| Natural Language Processing

About: This online course covers from the basic to advanced NLP and it is a part of the Advanced Machine Learning Specialisation from Coursera. You can enroll this course for free where you will learn about sentiment analysis, summarization, dialogue state tracking, etc. The topics you will learn such as introduction to text classification, language modelling and sequence tagging, vector space models of semantics, sequence to sequence tasks, etc. Upon completing, you will be able to build your own conversational chat-bot that will assist with search on StackOverflow website.

2| Natural Language Processing By Microsoft

About: This is a self-paced learning course which will give you a thorough introduction to the cutting-edge technologies applied to NLP. The duration of this course is 6 weeks where you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about statistical machine translation, deep reinforcement learning techniques applied in NLP, Vision-Language Multimodal language as well as Deep Semantic Similarity Models (DSSM) and their applications.

You will also learn how to apply deep learning models to solve machine translation and conversation problems, deep structured semantic models on information retrieval and natural language applications, deep reinforcement learning models on natural language applications and deep learning models on image captioning and visual question answering.

3| Natural Language Processing With Deep Learning

About: This is a lecture series on NLP provided by Stanford University where you will have an introduction to the cutting-edge research in deep learning applied to NLP. The minimum duration of the series is 1 hour and the topics included are NLP with deep learning, word vector representations, global vectors for word representation, word window classification and neural networks, backpropagation, dependency parsing, introduction to TensorFlow and other such related topics.

4| Natural Language Processing By Carnegie Mellon University

About: This course is provided by Carnegie Mellon University which covers a variety of ways to represent human languages (like English and Chinese) as computational systems and various ways to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarisation, extracting information, natural interfaces to databases, conversational agents, etc. The course includes some ideas central to Machine Learning and to Linguistics.

5| Deep Natural Language Processing

About: This is a GitHub repository which contains course on deep NLP by the University of Oxford in the form of lecture slides and videos. This course is focused on recent advances in analysing and generating speech and text using recurrent neural networks. You will be introduced with mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.

6| Natural Language Processing With Python

About: This is an e-book version of the book Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper. This book is more of a practical approach which uses Python version 3 and you will learn various topics such as language processing, accessing text corpora and lexical resources, processing raw text, writing structured programs, classifying text, analysing sentence structure and much more.

7| NLP For Beginners Using NLTK

About: This is a video series where you will learn about the basics of NLP through NLTK. The video basically concentrates on to the very useful feature in NLP called frequency distribution. You will learn how to calculate, tabulate and plot frequency distribution of words.

8| Speech And Language Processing

About: This is an ebook by authors Dan Jurafsky and James H. Martin where you will learn from the basics to advance of language processing. The topics included here are text normalisation, edit distance, regular expressions, language modelling, logistic regression, vector semantics, neural networks, neural language models, and other such related topics.