10 Free Top Notch Natural Language Processing Courses

Autumn is as good a season to learn natural language processing as any other, and why not do so with quality, free online courses?

This is a collection of just such free, quality online NLP courses, from such esteemed institutions of learning as Stanford, Oxford, University of Washington, and UC Berkeley. There are also offerings from independent sources like Yandex Data School, and even a short practical course on spaCy by one of its creators and co-founder of the company which steers its development.

So whether you are looking for theoretical or practical, or are a beginner or an advanced learner, the content included herein won't fail on living up to the promise of being 10 free top notch natural language processing courses. So dig in and learn NLP today.

From CS224n: Natural Language Processing with Deep Learning (Stanford)

 
1. A Code-First Introduction to NLP course
fast.ai

Our newest course is a code-first introduction to NLP, following the fast.ai teaching philosophy of sharing practical code implementations and giving students a sense of the “whole game” before delving into lower-level details. Applications covered include topic modeling, classfication (identifying whether the sentiment of a review is postive or negative), language modeling, and translation. The course teaches a blend of traditional NLP topics (including regex, SVD, naive bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture), as well as addressing urgent ethical issues, such as bias and disinformation. Topics can be watched in any order.

 
2. From Languages to Information
Stanford University

The online world has a vast array of unstructured information in the form of language and social networks. Learn how to make sense of it and how to interact with humans via language, from answering questions to giving advice!

 
3. Natural Language Processing with Deep Learning
Stanford University

Natural language processing (NLP) is one of the most important technologies of the information age, and a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc. In recent years, Deep Learning approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models.

 
4. Deep Learning for Natural Language Processing
University of Oxford

This will be an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course will cover 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. These topics will be organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications.

 
5. Natural Language Processing
University of Washington

• Fundamental goal: deep understand of broad language
  • Not just string processing or keyword matching
• End systems that we want to build:
  • Simple: spelling correction, text categorization…
  • Complex: speech recognition, machine translation, information extraction, sentiment analysis, question answering…
  • Unknown: human-level comprehension (is this just NLP?)

 
6. Natural Language Processing
Yandex Data School

• week 1 Embeddings
• week 2: Text classification
• week 3: Language Models
• week 4: Seq2seq/Attention
• week 5: Structured Learning
• week 6: Expectation-Maximization
• week 7: Machine translation
• week 8: Transfer learning and Multi-task learning
• week 9: Domain Adaptation
• week 10: Dialogue Systems
• week 11: Adversarial learning & Latent Variables for NLP
• week 12: Text Summarization

 
7. Natural Language Processing
National Research University Higher School of Economics (via Coursera)

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.

 
8. Applied Natural Language Processing
UC Berkeley

Students will apply and extend existing libraries (including scikit-learn, keras, gensim and spacy) to textual problems. Topics include text-driven forecasting and prediction (using text for problems involving classification or regression); experimental design; the representation of text, including features derived from linguistic structure (such as parts of speech, named entities, syntax, and coreference) and features derived from low-dimensional representations of words, sentences and documents; exploring textual similarity for the purpose of clustering; information extraction (extracting relations between entities mentioned in text); and human-in-the-loop interactive NLP. This class will focus both on modern neural methods for these problems (including architectures such as CNNs, RNNs, LSTMs, and attention) and on classical methods (logistic/linear regression, Bayesian models).

 
9. Advanced Methods in Natural Language Processing
Tel Aviv University

Natural Language Processing (NLP) aims to develop methods for processing, analyzing and understanding natural language. The goal of this class is to provide a thorough overview of modern methods in the field of Natural Language Processing. The class will not assume prior knowledge in NLP.

 
10. Advanced NLP with spaCy
Ines Montani (of Explosion AI)

In this course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches. I originally developed the content for DataCamp, but I wanted to make a free version so you don't have to sign up for their service. As a weekend project, I ended up putting together my own little app to present the exercises and content in a fun and interactive way.

 
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