Meet Aswini Thota, an Analytics and Artificial Intelligence (AI) leader who solves organisational and business problems leveraging data. He always believed in the power of data and amased what insights we can grasp from it. Over the course of his career, Aswini has developed a skill set in analysing data and he hopes to use his experience and expertise in data science to help people discover the amazing career opportunities that lie ahead in the field of Data Science. He has effectively evolved from a machine learning researcher to an award-winning AI / Data science leader. Aswini holds two master's degrees in Electrical Engineering and Data Science.

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Artificial Intelligence has already become an indispensable part of our everyday life, whether when we browse the Internet, shop online, watch videos and images on social networks, and even when we drive a car or use our smartphones. AI is widely used in medicine, sales forecasting, space industry and construction. Since we are surrounded by AI technologies everywhere, we need to understand how these technologies work. And for such understanding at a basic level, it is not necessary to have a technical or IT education. In this course, you will learn about the fundamental concepts of Artificial Intelligence and Machine learning.

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In this blog, you will learn about the best resources for obtaining completely free datasets for machine learning applications.

Artificial Neural Networks and Deep Learning are the most recent and advanced topics in machine learning, with several applications in many fields. They show promising results in many areas, from computer vision to drug discovery and stock market prediction. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using Artificial Neural Networks and Deep learning architectures in their projects. This course gives you some fundamentals of artificial neural networks and deep learning with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms using Python, TensorFlow, and Keras.

Then, we will start by loading the dataset on the devices in IID, non-IID, and non-IID and unbalanced settings followed by a quick tutorial on PySyft to show you how to send and receive the models and the datasets between the clients and the server. This course will teach you Federated Learning (FL) by looking at the original papers' techniques and algorithms then implement them line by line. In particular, we will implement FedAvg, FedSGD, FedProx, and FedDANE. You will learn about Differential Privacy (DP) and how to add it to FL, then we will implement FedAvg using DP. In this course, you will learn how to implement FL techniques locally and on the cloud. For the cloud setting, we will use Google Cloud Platform to create and configure all the instances that we will use in our experiments. By the end of this course, you will be able to implement different FL techniques and even build your own optimizer and technique. You will be able to run your experiments locally and on the cloud.

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Organized by Divine Fuh, HUMA – Institute for Humanities in Africa at the University of Cape Town, South Africa and Fanny Chabrol, CEPED-IRD, France and funded by Carnegie Corporation of New York and the Wenner-Gren Foundation, this workshop is located within the framework of the project Future Hospitals: 4IR/AI and the Ethics of Care at HUMA – Institute for Humanities in Africa headed by Divine Fuh, and the "Hospital Multiple" at CEPED-IRD headed by Fanny Chabrol. The workshop aims at proposing new ethnographic methodological and conceptual tools to think and imagine the "hospital of the future" in Africa, in particular, the way artificial intelligence (AI) seeks to transform and is currently transforming access to health care in hospitals today and in the coming years. Our project aims to build a problematisation of the hospital of the future and an ethnographic method to critically analyse the ethical, regulatory, and political issues with respect to AI, healthcare, and hospitals on the continent. We consider the "hospital of the future" – through the digitalization and computer automation of healthcare – as a global promise that needs to be challenged by ethnographic methods within hospitals, engaging with persons interacting with them. The first line of inquiry will challenge the logic of adoption and Africa as a place where development policies are implemented, where infrastructure projects are developed, in which technological innovation, mainly coming from the West, is presented as the promise of better health for those in need.