This training corresponds to a block of competences whose title reads "Artificial Intelligence Project Manager", currently under instruction at France Compétences. It will lead to a certification recognized and registered in the Répertoire national des certifications professionnelles (RNCP) and will therefore be eligible for the CPF.
This certification can be capitalized on over 5 years. You can choose to prepare the entire title progressively over time or have your complementary skills recognized by the VAE.
TARGET AUDIENCE AND PRESENTATION
This program is designed for professionals in the field of data science and computer science, or for any scientist and any technical or managerial profile with prior knowledge in linear algebra, probability, statistics and programming (Python), and who wish to acquire operational skills in data science and machine learning. This program teaches how to ethically and responsibly deploy artificial intelligence solutions. This program also aims to strengthen and foster collaborations between scientific teams and business departments.
The survey published in "The Human Impact of Data Literacy" of 2020  reveals that 74% of employees surveyed (9000 worldwide) feel overwhelmed by tasks involving their company's data. As a result, interest and productivity drop: 14% of them even prefer to avoid this kind of work altogether.
One of the major challenges of a company that wants to enhance its data is to homogenize " dataliteracy " within it. To do this, managers must know the potential of their data, its ethical dimension, and know how to support their employees in the development of their data knowledge (adoption of new technologies and software).
The skills to implement data solutions, strategic for your company.
Concrete experience with machine learning models and their applications to give a new dimension to your company.
The acquisition of a critical look at today's issues related to artificial intelligence, and in particular its ethical, legal and social stakes.
Module 1: Data-driven Decision Making
Take control of data visualization, exploratory data analysis, then exploit the power of data and its role in corporate decision-making.
The main concepts covered in this module are: decision making using data, data cleansing, data coding and data visualization using Python.
Module 2: Classical machine learning algorithms
To learn to know and use the fundamental techniques of machine learning, and to know how to gauge the impact of such tools in a company.
The main concepts covered in this module are: time series, clustering (data partitioning), and regression methods using scikit-learn.
Module 3: Ethics, bias and limitations of the learning machine
Explore the main challenges of machine learning, and the ethical and legal considerations of data use in the business world.
The main concepts covered in this module are: model evaluation, performance analysis, overlearning vs. underlearning, data augmentation, ensemble methods, and the ethical, legal and social issues of data use.
TRAINERS / TEACHERS
Expertise : Data science
Professor in Data science and Computer science | Academic Director