Tues, April 19, 2022 11-12pm CST Register: https://bit.ly/african-indaba-nlp Ife Adebara, PhD Candidate, African Languages, Computational Linguistics, & Deep Learning UBC, Vancouver, ATLI David Ifeoluwa Adelani, PhD Candidate, Africa NLP Universität des Saarlandes; Germany, Masakhane NLP The African DH Indabas are an avenue for occasional African DH discussions in which mostly Africa-based digital humanities scholars are in conversation with each other, or with colleagues from other parts of the world. The idea of the indaba suggests our commitment at AAAS, African Digital Humanities @KU, and IDRH-KU to creating a global community of leaders and experts in African digital humanities. Send questions to James Yeku at jyeku@ku.edu, powered by Localist, the Community Event Platform

The ratio is disturbed from being 1:1 to 4:5 for genuine to fake news. It is seen that the median length is lower for fake articles but it also has loads of outliers. It is seen that they start from 0 which is concerning. It actually starts from 1 when I used .describe() to see the numbers. So I took a look at these texts and found that they are blank.

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different methods of text preprocessing, and a way to estimate how much preprocessing you may need. For those interested, I've also made some text preprocessing code snippets for you to try. To preprocess your text simply means to bring your text into a form that is predictable and analyzable for your task.

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different methods of text preprocessing, and a way to estimate how much preprocessing you may need. For those interested, I've also made some text preprocessing code snippets for you to try. To preprocess your text simply means to bring your text into a form that is predictable and analyzable for your task.

While working on the Omdena PTSD challenge, as part of the company's AI for Good initiative, it quickly became clear to us that there are many challenges unique to the field of medicine that make it harder for chatbots to be implemented and generate value. However, through the power of community collaboration, we identified the most promising direction with great results. The scope of our chatbot depended on the ML and other challenge teams- after all, a chatbot is just a vehicle through which certain processes are sped-up and automated. After thoughtful discussions and investigations of the available data, which turned out to be very sparse (due to openness, privacy, etc.), we agreed on focusing our efforts on assisting professionals in screening refugees, veterans and other groups at high risk of PTSD and assessing the likelihood of an medical assistance being needed. Build a chatbot that will, through a conversation with people at risk, provide sufficient information for the Machine Learning team to make a PTSD risk assessment.

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different methods of text preprocessing, and a way to estimate how much preprocessing you may need. For those interested, I've also made some text preprocessing code snippets for you to try. To preprocess your text simply means to bring your text into a form that is predictable and analyzable for your task.

The following figure highlights the consistent and impressive performance of the NP Machine Learning model across the US market (Russell 3000) over the last 15 years.

Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for R: This link contains a curated list of R tutorials for Data Science, NLP and Machine Learning.

This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks.