| Sumario: | 2+ Hours of Video Instruction Overview Natural Language Processing LiveLessons covers the fundamentals of natural language processing (NLP). It introduces you to the basic concepts, ideas, and algorithms necessary to develop your own NLP applications in a step-by-step and intuitive fashion. The lessons follow a gradual progression, from the more specific to the more abstract, taking you from the very basics to some of the most recent and sophisticated algorithms. About the Instructor Bruno Goncalves is currently a Senior Data Scientist working at the intersection of Data Science and Finance. Previously, he was a Data Science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Universite. Since completing his PhD in the Physics of Complex Systems in 2008 he has been pursuing the use of Data Science and Machine Learning to study Human Behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading. In 2015 he was awarded the Complex Systems Society's 2015 Junior Scientific Award for “outstanding contributions in Complex Systems Science” and in 2018 is was named a Science Fellow of the Institute for Scientific Interchange in Turin, Italy. Skill Level Intermediate Learn How To Represent text Model topics Conduct sentiment analysis Understand word2vec word embeddings Define GloVe Apply language detection Who Should Take This Course Data scientists with an interest in natural language processing Course Requirements Basic algebra Calculus and statistics Programming experience Lesson Descriptions Lesson 1: Text Representations The first step in any NLP application is to establish the representations of text and numbers. One-hot encodings provide us with a sparse approach to representing words and n-grams, while bag-of-words improves memory efficiency even further. Naturally, not all words are meaningful, so the next steps are to remove meaningless stop words and to identify the most relevant words for our application using term frequency/inverse document frequency (TF/IDF). Finally, the lesson covers how to identify the stems of words so you can meaningfully reduce the size of your vocabulary. Lesson 2: Topic Modeling Lesso...
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