Ecolinguistics and frugal innovation
Table of Contents
What you will learn
At the end of the course, participants will be able to apply NLP for research or innovative projects, compare, explain and choose different methods, as well as question and test hypothesis. (Optional) Through practical use cases, participants will learn how to use Python to analyze data, visualize information and convey learnings with scikit-learn, NLTK, gensim or spacy to build simple proof of concepts and MVP.
Course duration: 16 hours. 1h20 per week, for 12 weeks.
Level: Basic to advanced.
Topics: Computational linguistics, learning paradigms, models & limits, NLP.
(Recommended) Audience: Master students enrolled in a scientific field (e.g., data science, neuroscience, psychology), language learning and development practitioner (e.g., orthophoniste, teacher).
Motivate language studies.
Give historical context.
Encode, decode information and meaning.
Combine analytical & synthetic skills.
Compare learning paradigms in NLP.
Create to understand.
Use language for social innovation.
Use language for environmental protection.
Identify fake news, counter hate speech.
Give and receive feedback.
Are there prerequisites?
There are no prerequisites for the first course. A computer with Python installed and a Framagit account is required if you wish to experiment with the use cases. Statistics, maths and python libraries will be introduced along the course with practical use cases.
How often do the courses run?
Continuously, at your own pace. Some live webinars and cohorts will be scheduled to foster a dynamic learning environment and sense of community.
I followed CS224n at Stanford. Should I attend this course?
Yes! CS224n is essentially about deep learning which isn’t what this course is about. This course will give you new insights and ways to approach problems.
I worked in computational linguistics and designed simple solutions in the past. Could I share my learnings with you?
Yes of course! Feel free to reach out to chat and co-create the course. Thanks!