Transformers have revolutionized NLP since their inception in 2017. They can understand language at a deeper level as well as parallelize training on GPUs. In this course, you will learn how to create Transformers for NLP.

You've heard that NLP will disrupt just about everything, but you're not sure what that means for the ways you handle data. This course will teach you how NLP can be successfully adopted for real-world data workflows across multiple industries.

Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.

Learn how to load, transform, and transcribe speech from raw audio files in Python.

This course will teach you all the NLP techniques in Tensorflow 2 needed to do amazing things like generate text or classify intention.

Learn the bag of words technique for text mining with R.

Learn the core techniques of computational linguistics alongside the cognitive science that makes it all possible and the ethics we need to use it properly.

Extend your knowledge of the core techniques of computational linguistics by working through case-studies and visualizing their results.

Do you want to learn how machines can learn tasks we thought only human brains could perform? Then take this Deep Learning course developed by IVADO, Mila and Université de Montréal: an extensive overview of the essentials of deep learning, this ground-breaking technology already prevalent in our lives and spanning all sectors.

In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.