You've already made the first step - data collection. You've gathered lots of it but don't know how to utilise it in order to predict, optimise and control the future. Infotiv has developed a new hands-on course to get you started on your quest for mining value out of your precious data.
The data doesn't lie, and you know it. Applying machine learning algorithms to analyse your data enables you to extract complex information otherwise not possible to comprehend.
With trained software instead of coded software you can develop truly smart functionality. Let data drive your decisions and you'll be ahead of the curve. Data-driven businesses provide more value to their customers while keeping development costs low.
Course description
This course aims to give deep knowledge about Machine Learning (ML):
- What it is.
- What it is not.
- When to apply it.
- How to apply it.
Upon completion of the course, the user should be comfortable in training and applying ML.
- The course covers every stage of the Data Science Lifecycle, including verification & validation of ML.
- Learn how to put Machine Learning into practice and how to verify your Machine Learning solution.
- Apply ML in practice, both virtually and on embedded target environment.
Prerequisites
To get the most from the course basic knowledge in Python as well as in concepts surrounding software development is desired.
Course content
- Introduction to AI/ML
- Development environment
- Selection of algorithm and training
- Data management
- Verification & Validation of ML functionality
- Apply ML in practice, both virtually and on embedded target environment
- Risks surrounding the use of ML
Course language
The course will be held in english.
Course Schedule
During the course, the participants will be able to practice on all steps necessary to train, test (verify), integrate (on HW), and validate a machine learning model.
The exercises will be split over the two days. The first day covers exercise setting up the workflow and training an initial model and the second day handles testing and deployment. After completing the course, each participant will be allowed to bring home the Raspberry Pi containing their trained and tested model.
Click here to see a detailed schedule for the 2 days
Course completion
The Infotiv Machine Learning course gives the student the theoretical and practical knowledge in training models. The student will also get basic understanding on the challenges in testing functionality based on ML.
Estimated completion time: 2 days
Next course dates
The next "Machine Learning – from Zero to Hero" courses will take place Infotivs office at Västra Hamngatan 8 in Gothenburg on:
- December 20 and 21, 2022
Price
The price for one (1) person is: SEK 16.500.
Customized course for your company
Are you interested in a course customized to your company? Of course, we offer you that. Please contact us for a quote.
Registration
Send an email to tomas.sjogren@infotiv.se to either sign up or ask for more details.
Course leaders
Martin Karsberg:
Competence Leader for Test Methodologies at Infotiv. Working with testing of IT in a variety of fields, from the defence industry to municipalities. Martin also teaches methodology.
Martin knows his way around both traditional and new testing methods. He is currently Infotiv's project leader for two research projects focused on the task of V&V on Machine Learning.
During weekends Martin listens to his broad collection of LP:s, grill burgers and lets his mind wander back to his punk days in Ireland.
Elias Sonnsjö:
Competence Leader for Machine Learning/AI at Infotiv. Elias has a genuin interest and wide knowledge within the field of Machine Learning. This started during the work with his master thesis, focusing on modeling of virtual sensors and sensor fusion using ML.
Over the last two years Elias has been a part of Infotivs´ research group with focus on V&V of safety critical functionality based in ML Technologies.
Outside Machine Learning Elias have done guest lectures at Chalmers along with crushing it on stage with his metal band.
