Natural language processing, otherwise known as NLP, is the technology behind Siri, autocorrect, chatbots, and Google Translate. It’s what helps you translate text, filter spam, and detect fake news. In short, this technology 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 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 prepare it for further exploitation by a computer.
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 a more powerful vectorization technique called word embeddings and apply it to infer meaning from a text.
Once you complete this course, you will have a basic understanding of how NLP models work and how to use them in machine-learning projects. We will also introduce you to the spaCy 3.4, scikit-learn 1.1, and NLTK 3.7 libraries in Python 3.10.
Ready to dive into one of the most innovative domains in artificial intelligence? Then let’s get started!
