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.

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (Azure) - Deploying AI & ML Models in Production using Microsoft Azure Machine Learning

As the big data era unfolds, developments in sensor and information technologies are evolving quickly. As a result, science and businesses are yielding enormous amounts of data. Ideally this data provides valuable insights for decision-making in real time. But processing data the traditional way is no longer possible. Join Wageningen University & Research, #1 university Animal Sciences and Agriculture, and learn how to best handle big data sets. Enrol now.

Become a UX data scientist! From qualitative data analysis to big data Web analytics, you will be able to leverage insights from data to make empirically-based recommendations.

Further develop your knowledge of big data by applying the skills you have learned to a real-world data science project.

In this class, we will help you understand how to create and operate a data lake in a secure and scalable way, without previous knowledge of data science!

What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions.

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.

Learn about the methodology, practices and requirements behind data science to better understand how to problem solve with data and ensure data is relevant and properly manipulated to address a variety of real-world projects and business scenarios.

This course presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine and how to exploit it for research and in the clinic. The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this field.

Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.

Learn the fundamentals of Artificial Intelligence with a combination of AI fundamentals and an overview of the rich Linux Foundation AI & Data projects ecosystem. It is well-suited for any kind of AI adopter, regardless of the professional background and level of technical knowledge.

The term “Computation” refers to the action performed by a computer. A computation can be a basic operation and it can also be a sophisticated computer simulation requiring a large amount of data and substantial resources. This course aims at introducing learners with no prior knowledge to the basic key concepts of computer science. By following the lectures and exercises of this course, you will gain an understanding of algorithms by programming using the language Ruby.

Learn about the most popular data science tools, including how to use them and what their features are.

Our future is here and it relies on data. Medical robots, smart homes and cities, predictive policing, artificial intelligences – all are fuelled by data and all promise new benefits to society. But will these innovations benefit everyone? Who stands to gain and who is put at risk? How can we ensure that data is part of a just and sustainable world?

The second course in Python for Everybody explores variables that contain collections of data like string, lists, dictionaries, and tuples. Learning how to store and represent and manipulate data collections while a program is running is an important part of learning how to program.

Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems.

Learn the fundamental techniques for data analytics and to be prepared for learning and applying more advanced big data technologies.

Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner.

Sentar las bases de conocimiento en R y aprender a discutir, analizar y visualizar datos.

Learn to use R programming to apply linear models to analyze data in life sciences.

Learn how to go from raw data to meaningful insights using AWS with this one-week course.

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.