Regression is arguably the most widely used machine learning technique, commonly underlying scientific discoveries, business planning, and stock market analytics. This learning material takes a dive into some common regression analyses, both simple and more complex, and provides some insight on how to assess model performance.

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.

Explore transformer models, including BERT, and their applications in natural language processing using Google Cloud.

This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

This course helps you master the topic of transformers for natural language processing, including the architecture, training, and prompt design

Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language, and returning translations of the transcription in one or more other languages.

The Translator service enables you to create intelligent apps and services that can translate text between languages.

AI for Teachers is an activity focused, self-paced course designed to help educators prepare themselves and their students for a future which is becoming increasingly automated

UiPath 2020. Robotic Process Automation Introduction

Discover how even computer algorithms may be biased and have a serious impact on our every day lives. In this MOOC, based on an IVADO School involving various international experts in the field, you will learn how to identify and alleviate bias and discrimination in Artificial Intelligence.

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.