Natural Language Processing, otherwise known as NLP, is the technology behind Siri, autocorrect, chatbots, and Google Suggest. It's what helps you translate text, filter spam, and detect fake news. In short, it's the technology that allows a machine to understand and process human language.
But how does it work under the hood? How can you use NLP to transform human language into something that a computer can understand? Look no further; this course has the answer!
In Part 1 of this course, we will explore how to preprocess text data and get it ready for further processing.
In Part 2, we will explore a text vectorization technique called bag-of-words and solve text classification problems such as sentiment analysis.
In Part 3, you will learn another vectorization technique called word embeddings and apply it to text exploration.
By the end of the course, you will have a basic understanding of how NLP models work and how you can use them in machine learning projects. You will also be introduced to the spaCy 2.3 and NLTK 3.5 libraries in Python 3.8!
Ready to dive into one of the most innovative domains in artificial intelligence? Then let's get started!
Meet Your Teacher
Alexis Perrier is a Data Science consultant specializing in Natural Language Processing and Machine Learning. He holds a PhD in signal processing from Telecom Paris and is the author of several books on machine learning in the cloud. He is also a professor at Ecole Polytechnique and Université Gustave Eiffel, as well as a regular contributor to meetups and online publications. You can find more of his daily data science musings on Twitter @alexip.
