Modern NLP in Python w/ Patrick Harrison

Modern NLP in Python

Academic and industry research in Natural Language Processing (NLP) has progressed at an accelerating pace over the last several years. Members of the Python data science community have been hard at work moving cutting-edge research out of papers and into open source, "batteries included" software libraries that can be applied to practical problems.
In this tutorial and live demo, we'll explore some of these tools for modern NLP in Python, including spaCy and gensim. Along the way, we'll learn about recent foundational advances in machine natural language representations, such as topic modeling with Latent Dirichlet Allocation (LDA) and word vector embedding with word2vec. Finally, we'll discover visualization tools to help us introspect and understand high-dimensionality natural language models, including pyLDAvis and t-SNE.

About Patrick Harrison:

Patrick started the Data Science team at S&P Global Market Intelligence, a business and financial intelligence firm and data provider, and serves in the role of Lead Data Scientist there. Working in a major company for which data is both the primary raw material and finished product provides an unusually broad, target-rich environment for data scientists. The Data Science team at S&P GMI employs a wide variety of data science tools and techniques, including machine learning, natural language processing, recommender systems, graph analytics, among others.
Prior to joining S&P Global Market Intelligence, Patrick received degrees in Economics (BA) and Systems Engineering (MS), both at the University of Virginia. His graduate research focused on complex systems and agent-based modeling.

About Data Hackers:

RVA Data Hackers is a community of programmers who meet regularly to develop skills and learn about the tools and techniques of Big Data. We discuss how to find, organize, understand and serve data sets large and small. We'll cover anything related to 'big data' -- machine learning, artificial intelligence and architectures to scale Big Data for the Internet. If you're a programmer interested in machine learning algorithms and managing big data, this group is for you. Topics vary from basic concepts to demonstrations of real-world implementations and everything in between. Our mission is to foster a local community of experienced, practicing experts. We're here to have fun, share and learn about an exciting field of computer science.