new Python & R courses!

This week, it's all about language: Learn Natural Language Processing in Python and Sentiment Analysis in R: The Tidy Way! Build a "fake news" classifier, and analyze text data from Twitter, Shakespeare, TV news, pop music, and more! You learn how to use regular expressions, and concepts like word tokenization and topic identification. [DataCamp]( New Courses! [NLP in Python]( Natural Language Processing Fundamentals in Python Taught by Katharine Jarmul, Founder of kjamistan Learn Natural Language Processing (NLP) basics, build your own fake news classifier, and utilize deep learning to solve common NLP problems. [Play now ›]( Sentiment Analysis in R: The Tidy Way Taught by Julia Silge, Data Scientist at Stack Overflow Develop your text mining skills using tidy data principles. You will apply your new skills by performing sentiment analysis in several case studies, on text data from Twitter to TV news to Shakespeare. [Play now ›]( NLP Fundamentals in Python: What You'll Learn Chapter 1: Regular expressions & word tokenization This chapter will introduce some basic NLP concepts, such as word tokenization and regular expressions. Chapter 2: Simple topic identification Using basic NLP models, you will identify topics from texts based on term frequencies. Chapter 3: Named-entity recognition Learn how to identify the who, what and where of your texts using pre-trained models on English and non-English text. Chapter 4: Building a "fake news" classifier Apply what you've learned along with some supervised machine learning to build a "fake news" detector. [Play Now ›]( Sentiment Analysis in R: What You'll Learn Chapter 1: Tweets across the United States In this chapter you will implement sentiment analysis using tidy data principles using geocoded Twitter data. Chapter 2: Shakespeare gets Sentimental Use tragedies and comedies by Shakespeare to show how sentiment analysis can lead to insight into differences in word use. Chapter 3: Analyzing TV News Explore a dataset of closed captioning from television news. Chapter 4: Singing a Happy Song (or Sad?!) In this final chapter, you will explore pop song lyrics that have topped the charts from the 1960s to today. [Play Now ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02140 [Unsubscribe](