Your first NLP project: peaks and pitfalls of unstructured data

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Natural Language Processing improves the quality of your text data forfuture analysis and increases the accuracy of your machine learningmodel. It’s important to know what goes into the bag of words and whatare some potential do's and don'ts of text pre-processing. Which textnormalization steps are necessary and which ones are “nice-to-have”? Whyis classic NLP still relevant in the age of Deep Learning? What metricscan be used to compare word frequencies and what can machine learningalgorithms do with those numbers? This NLP talk provides answers tothese questions and more! You'll see three examples of NLP pipelinesusing spaCy: sentiment analysis and emoji in tweets, named entityrecognition in Yelp reviews, and multilingual topic modeling for newsarticles.
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