Applied Machine Learning

You will start with an introduction on how to link business to data, and data to Machine Learning, followed by a deep-dive into Supervised and Unsupervised Machine Learning algorithms. Over 6 weeks, you will work on several use-cases and acquire experience on the following topics:

1. How to convert a business problem into a Machine Learning problem?
2. How to define requirements for a Machine Learning project (including key performance indices) using an ML canvas?
3. In-depth, hands-on experience in creating different types of Machine Learning pipelines (supervised vs. unsupervised), including data transformation, feature engineering, building a data pipeline, hyper-parameter tuning, loss functions, and cross-validations on several regression and classification tasks
4. Hands-on experience in identifying bias and fairness of Machine Learning problems and Machine Learning model explainability
5. Designing and solving several Machine Learning use-cases, (ex: predictive maintenance, churn prediction, customer segmentation).

Schedule:

• ● This remote course runs for 6 weeks on Monday and Wednesday from 16:00 to 19:00.
• ● Every week will contain lecture and exercise modules designed to give you as much hands-on experience as possible.