Knowledge Transfer in our DNA
Knowledge Transfer is essential for us, we run regularly workshops and training courses across a range of data science and AI subjects. Our workshops are flexible in terms of courses duration and/or in the way how can they be delivered: in site (at your premises) or remotely.
Our team of instructors is made up of experts who have many years of experience in knowledge-transfer whether as lecturers at universities and grandes écoles or at their previous companies. In addition, Our experts have proven real hands-on experiences as consultants on various data science and AI projects in both theoretical and practical aspects of the following topics.
In this course we address the following questions: What knowledge and skills are needed to be able to identify data sources, know how to collect information, know how to ensure its quality (and clean it up if necessary) so that the data is relevant according to the case ‘business use?
This course is designed to help you make sense of basic probability and statistics with easy-to-understand explanations of all the subject’s most important concepts. Whether you are starting from scratch or if you want to improve your skills in statics and struggling with your assigned textbook or lecture material, this workshop was built with you in mind.
Machine learning is a technology that allows computers to learn not by being explicitly programmed but by relying on a study of data from the past on the one hand and also of the study of new data in order to acquire new knowledge and thus improve themselves over time.
These data-learning technologies call upon algorithms which we propose to study here. Thus, our course is a presentation of the various classic algorithms of machine learning (supervised & unsupervised) and also of neural networks for deep learning.
This course is a quick presentation of the different big data environments (Hadoop, Spark) and their links with the proper functioning of machine learning algorithms. Here we learn best-practice for deploying machine learning models. We will see how to set up the industrialization of the model in a development environment and then in a production environment.