This training is perfect for:

Marketing professionals with programming experience
Professionals in QM with programming experience
Professionals in machine maintenance with programming experience
Participants will learn how to:

Choose the best-suited ML method for a given problem
Train an ML algorithm
Evaluate the algorithms performance
Fine-tune hyperparameters

Agenda:

Performance metrics:
Participants get to know how the performance of an ML algorithm can be measured and what needs to be taken into account.
 
Regression methods:
In this section, the first ML methods are introduced, both for regression as well as classification problems. Participants will set benchmarks against which all other algorithms will be measured.
 
Support vector machines:
Here, participants will get to know a powerful and widely applicable ML method.
 
Decision trees:
This is the third type of ML method that participants will learn about.
 
Random forests:
A single decision tree is often not sufficient. Therefore, participants will learn how to „combine“ several trees into a random forest. This requires considerable compute power.
 
Model evaluation & hyperparameter tuning:
Participants learn how to decide which model performs the best and how they can improve the best-performing model even further.
 
Ensemble learning:
With even more compute power, different ML methods can be combined to improve performance even further.
 
Outlook
In this final topic, participants will get a glimpse of what can be done with deep learning – the next step in the ML journey.

Course format:

The lectures will be held online from 10:00 – 16:00 CEST over the course of two days. The participation links will be provided after the purchase and before the course.

Prerequisites:

The participants are expected to have at least basic programming skills in Python. The programming language of choice in this course is Python with libraries such as NumPy, Pandas, Scikit-Learn and Matplotlib.

Hands-on labs:

Participants will use their own laptops or workstations to do the hands-on exercises. The content is delivered with Jupyter notebooks on Google Colab, so participants should have a Google account in order to be able to participate fully.

Lecturer:

Simeon Harrison (EuroCC Austria and VSC Research Center, TU Wien)

Price:

Full price for the course with course documentation: 240 EUR/person (including VAT)

Certification:

Upon completion of the online course, participants will receive a certificate of attendance.