Two day trainings ‘Introduction to Responsible ML in R or Python’— schedule for 2022

Based on our experience in the area of Responsible Machine Learning, developed a unique two-day hands-on training. Jump into the topic of eXplainable Artificial Intelligence with our trainers.

The training is conducted once a month in small groups online. Small groups encourage questions and the interactions within the group. Dates of upcoming trainings in 2022:

• 21–22 January,
• 18–19 February,
• 18–19 March,
• 22–23 April,
• 20–21 May.

Agenda

Day 1: We will start with a brief introduction to predictive modelling, then discuss tree based methods and ensembles like random forest and gradient boosting. We will end the day by discussing the hyperparameter tuning procedure.

Day 2: We will start the second day with an introduction to explainable artificial intelligence. We discuss variable importance followed by Partial Dependence profiles, Shapley values, the LIME method and Ceteris Paribus profiles.

Each block includes a presentation on the method, a hands-on session and Q&A.

Prerequisites

This is an introductory training. We assume that participants have a basic knowledge of the language in which the training is conducted (R or Python). They can load data, perform simple EDA and build simple predictive model, e.g. linear regression model.

Language

Depending on the group’s preference, the hands-on part can be carried out in R (mlr3 + DALEX) or Python (scikit-learn + dalex).

The methodology part does not depend on the language.

Price

Price of the two-day online training: 899 EUR / 999 USD.

Discounts available for groups of more than one person.

If you are interested in private training for a group from your company please contact with us.

Each participant will receive a paper materials in a form of a comic book https://betaandbit.github.io/RML/.

Book your training

To book a training please contact with trainings@solutions42.ai.

Two day trainings ‘Introduction to Responsible ML in R or Python’— schedule for 2022 was originally published in ResponsibleML on Medium, where people are continuing the conversation by highlighting and responding to this story.