It will be aimed at healthcare professionals (doctors, pharmacists, medical physicists, etc.), students and researchers in biology and healthcare as well as biomedical engineers wishing to develop AI projects using medical data. The first class will follow this training in November 2021. The 3IA Côte d'Azur, whose main focus is on digital health, has announced the launch of its new university diploma (DU) in Artificial Intelligence and Health. This initiative is part of the institute's strategy, as Olivier Humbert, a doctor and professor of Nuclear Medicine & Biophysics at the Université Côte d'Azur, explains: "At 3IA Côte d'Azur, in terms of digital health, we are working to bring together national and international experts and professionals from different disciplines to advance research. The Soph.I.A Summit, which will be held next November, is a good example of this. All together, let's join the movement of a French start-up Nation and contribute to the evolution of the Medicine of tomorrow."

I'm super excited to introduce the new panel data forecasting functionality in modeltime. Just say NO to for-loops for forecasting. Fitting many time series can be an expensive process. The most widely-accepted technique is to iteratively run an ARIMA model on each time series in a for-loop. Organizations now need 1000's of forecasts.

Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS). Open Educational Resources (OER) are a crucial factor for greater educational equity. In this paper, we describe the specific requirements for OER in ML and DS and argue that it is especially important for these fields to make source files publicly available, leading to Open Source Educational Resources (OSER). We present our view on the collaborative development of OSER, the challenges this poses, and first steps towards their solutions. We outline how OSER can be used for blended learning scenarios and share our experiences in university education. Finally, we discuss additional challenges such as credit assignment or granting certificates.

Hi, Welcome to The Ultimate Python 3.9 Programming 2021 A-Z MasterClass. Python is currently used for everything. Although it is very easy to learn, but, it is very useful and powerful. Its fields are so many, and by learning python, it will be very easy for you to get higher Jobs in the largest companies such as Google, Dropbox, Spotify and many more. Simply you can do multi scale tasks with python, because it is multipurpose professionally and quickly with fewer lines of code.

Tableau Desktop- Beginner Start your Tableau journey here by learning the basics and learn how to quickly digest big data today. With nearly 10,000 training videos available for desktop applications, technical concepts, and business skills that comprise hundreds of courses, Intellezy has many of the videos and courses you and your workforce needs to stay relevant and take your skills to the next level. Our video content is engaging and offers assessments that can be used to test knowledge levels pre and/or post course. Our training content is also frequently refreshed to keep current with changes in the software. This ensures you and your employees get the most up-to-date information and techniques for success.

This course explores a specific domain of data science: time series analysis. The lectures explain topics in time series from a high level perspective, so that you can get a logical understanding of the concepts without getting intimidated by the math or programming. Whether you are new to time series or an experienced data scientist, this course covers every aspect of time series. The later half of the course entails several projects for you to get your hands dirty with time series analysis in Python. You will learn about modern time series forecasting models and AI, how to build them, and implement them to do extraordinary things.

Gift This Course What you'll learn Description Welcome to The Complete 2021 Android Machine Learning Course. In this course, you will learn the use of Machine learning in Android without knowing any background knowledge of machine learning. In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used. So it's important to learn how we can integrate ML models inside Android applications.

Gradio is free and open source python library . We can quickly and easily create UI interfaces within our python notebook,or share with anyone with just few lines of code and demonstrate our finished model results. Gradio helps quickly create customizable UI components within colab, jyupter notebook or scripts and around TensorFlow or PyTorch models, or even arbitrary Python functions. Gradio installation is fast and easy to setup .You can install Gradio using pip command.

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.

This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections. We start with linear random projection and then justify its correctness by JL lemma and its proof. Then, sparse random projections with $\ell_1$ norm and interpolation norm are introduced. Two main applications of random projection, which are low-rank matrix approximation and approximate nearest neighbor search by random projection onto hypercube, are explained. Random Fourier Features (RFF) and Random Kitchen Sinks (RKS) are explained as methods for nonlinear random projection. Some other methods for nonlinear random projection, including extreme learning machine, randomly weighted neural networks, and ensemble of random projections, are also introduced.