Abstract: 

Extracting knowledge from text data has always been one of the most researched topics in machine learning, but only recently we witnessed breakthroughs that put NLP in the spotlight. Many pieces of information are stored in unstructured data, like text, which is extremely important in many different fields, from finance to social media and e-commerce.

In this course we will go through Natural Language Processing fundamentals, such as pre-processing techniques,tf-idf, embeddings, and more. It will be followed by practical coding examples, in python, to teach how to apply the theory to real use cases.

The goal of this workshop is to provide the attendees all the basic tools and knowledge they need to solve real problems and understand the most recent and advanced NLP topics.

Bio: 

Laura Skylaki is a Manager of Applied Research in Thomson Reuters Labs, where she leads advanced machine learning projects in the domain of Legal and Tax AI.With a career spanning more than a decade at the intersection of research and practical application, she has contributed technical expertise in diverse fields such as bioinformatics and stem cell biology, image processing and natural language processing. She holds a doctorate in stem cell bioinformatics from the University of Edinburgh, UK, and has been publishing on machine learning applications in leading academic journals since 2012.