Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Managing Editor, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
You've seen their Big Bad NLP Database and The Super Duper NLP Repo. Now Quantum Stat is back with its most ambitious NLP product yet: The NLP Model Forge.
Amazon's Machine Learning University is making its online courses available to the public, starting with this Accelerated Natural Language Processing offering.
Take part in the latest KDnuggets poll, and share your insights with the community. Which Data Science skills do you currently possess, and which are you looking forward to add or improve upon? Vote now!
This curated collection of 5 natural language processing books attempts to cover a number of different aspects of the field, balancing the practical and the theoretical. Check out these 5 fantastic selections now in order to improve your NLP skills.
Interested in learning more about computational linear algebra? Check out this free course from fast.ai, structured with a top-down teaching method, and solidify your understanding of an important set of machine learning-related concepts.
Perhaps it's time to take a look at this relatively new offering from Stanford, Ethical and Social Issues in Natural Language Processing (CS384), an advanced seminar course covering ethical and social issues in NLP.
As has become tradition on KDnuggets, let's start a new week with a new eBook. This time we check out a survey style text with a variety of topics, Foundations of Data Science.
For this week's free eBook, check out the newly released Deep Learning with PyTorch from Manning, made freely available via PyTorch's website for a limited time. Grab it now!
This week's free eBook is a classic of data science, An Introduction to Statistical Learning, with Applications in R. If interested in picking up elementary statistical learning concepts, and learning how to implement them in R, this book is for you.