Data Science

AI is not only for engineers. “AI for Everyone”, a non-technical course, will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today’s AI can – and cannot – do. Finally, you will understand how AI is impacting society and how to navigate through this technological change.

Discover some of the ways machine learning is helping improve lives and grow businesses with this free online course from Google.

Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms with this free online course from the University of Adelaide.

In this course, you'll define clustering for ML applications, prepare data for clustering, define similarity for your dataset,  compare manual and supervised similarity measures, use the k-means algorithm to cluster data, evaluate the quality of your clustering result.

This specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data.

In this course, you'll build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

This free, two-hour deep learning course provides an interactive introduction to practical deep learning methods. You will learn to use deep learning techniques in MATLAB® for image recognition.

In this free TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods.

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. Taking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones.

In this course on global statistics, which the University of Geneva offers jointly with the ETH Zürich KOF, the participants learn about the general approach to constructing composite indices and some of the resulting problems.

Through the preparation for HCIA-AI, you will systematically understand and grasp Python programming, essential mathematics knowledge in AI, basic programming methods using TensorFlow (a machine learning and Deep Learning platform framework), pre-knowledge and overview of Deep Learning, overview of Huawei cloud EI.