Machine Learning with Python
Many modern digital applications increasingly rely on machine learning as a means to derive predictive strength from high-dimensional data sets. Compared to traditional statistics, the absence of a focus on scientific hypotheses, and the need for easily leveraging detailed signals in the data require a different set of models, tools, and analytical reflexes.
This course is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be
This course aims to bring participants to the level where they can independently tackle the analytical part of data mining projects. This means that the most common types of projects will be addressed - regression-type with continuous outcomes, classification with categorical outcomes, and clustering. For each of these, the practical use of a set of standard methods will be shown, like Random Forests, Gradient Boosting Machines, Support Vector Machines, k-Nearest-Neighbors, K-means,... Furthermore, throughout the course, concepts will be highlighted that are of concern in every statistical learning applications, like the curse of dimensionality, model capacity, overfitting and regularization, and practical strategies will be offered to deal with them, introducing techniques such as the Lasso and ridge regression, cross-validation, bagging and boosting. Instructions will also be given on a selection of specific techniques that are often of interest, such as modern visualization of high-dimensional data, model calibration, outlier detection using isolation forests, explanation of black-box models,... Finally, the last lecture will introduce the idea of deep learning as a powerful tool for data analysis, discussing when and how to practically use it, and when to shy away from it.
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Machine Learning with Python
- Type of course: This is an on campus course.
- Dates & times: April 25, May 2, 9, 16, 23 and 30, June 13, 2022, from 5.30 pm to 9 pm
- Venue: UGent, Faculty of Sciences, Campus Sterre, Krijgslaan 281, building S9, 9000 Gent
- Target audience: This course targets professionals and investigators from all areas that are involved in predictive modeling based on large and/or high-dimensional databases.
- Exam/certificate: Participants who attend all classes receive a certificate of attendance via e-mail at the end of the course. Additionally, participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued. The exam consists of a take home project assignment. Students are required to write a report by a set deadline.
- Course prerequisites: Participants are expected to be familiar with basic statistical modeling (as for instance taught in Module 2 - Drawing Conclusions from Data: an Introduction of this program), and to have a had a first experience programming in Python (as for instance taught in Module 4 - Getting Started with Python for Data Scientists of this year's program).
- Funding: => Our academy is recognised as a service provider for the 'KMO-portefeuille'. In this way small and middle sized businesses located in the Flanders region can save up to 30% on the registration fee for our courses. You can request this subsidy via www.kmo-portefeuille.be up until 14 calender days after the course has started. => UGent PhD students can apply for a full refund from their Doctoral School.
- Reduction: => If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment => Reduced prices apply to coworkers in governmental institutions, non-profit organisations and higher eduction as well as for students and the unemployed.
- Enrolling for this course is possible via the IPVW-ICES website.