February 15, 2022
DIMECC Machine Learning Academy (MLA) includes diverse learning modules from ML algorithms to ethics, and from designing and managing artificial intelligence (AI) projects to implementing AI in the company business. The goal of the training is to increase the participants’ understanding of how to utilize AI and machine learning in their company. After the course, participants will have understanding of the fundamentals of machine learning, as well as the ability to recognize and manage development tasks that aim to benefit from machine learning.
Machine Learning Academy in a Nutshell:
YOU WILL GET:
- Basic understanding of the principles, methods, abilities and limitations of AI and ML, as well as the current status of related commercial platforms, tools, and implementations.
- Practical grounding in ML, its technologies, and business applications equipping you with the needed knowledge to drive further all related development activities in your company.
- Ability to specify, design, and lead activities aiming at applying ML methods, algorithms, and technologies.
- An initial plan on when, where, and how you could deploy AI and/or ML methods and technologies in your businesses.
Example practical result example from earlier MLA courses:
“One concrete example was Ponsse’s field project, which focused on after-sales services, especially field maintenance of the harvesting equipment where Machine Learning was to be used to recognize the needed oil change interval. Hydraulic oil and filters are currently changed at fixed intervals, approximately every 1800 hours, and optimized change interval would mean remarkable savings.”
DIMECC Machine Learning Academy is aimed for professionals working in the industry.
- R&D supervisors/Business development managers managing AI/ML development projects
- R&D engineers participating in AI/ML development projects
- Business and product owners participating in AI/ML development project
- Employees, who specify and source work and subcontracting related to AI & ML
Diverse curriculum takes comprehensive approach to machine learning.
The course uses a combination of theory, discussion, practical exercises and examples of existing application and business cases to emphasize the application of the methods and concepts.
Target is to have practical exercises from companies live business environment.
- Understanding of engineering mathematics (e.g. linear algebra, statistics, probabilities, functions, basics of optimization theory)
- Open mind and initial understanding how my company’s business would benefit from deployment of AI and ML methods
- Previous programming experience is not required, but having it will help the participant gain more from the training
The course will cover the following topics:
Basics of Artificial Intelligence and Machine Learning
- Understanding the basics of AI and ML, and their effects on business
- Understanding what machine learning really is
- Recognizing when to apply machine learning and what sort of tasks it is suited for.
- Learning how to apply linear and non-linear regression and classification.
- Understanding how to use supervised learning to solve business problems.
- Learning basics and most important practical concepts in neural networks.
- Understanding how to use neural networks to solve business problems.
- Learning how to apply clustering and dimensionality reduction.
- Understanding how to use unsupervised learning methods to solve business problems.
Machine Learning Projects
- Understanding the flow of a typical ML project.
- Understanding roles and communication needed in a machine learning projects.
- Learning how and why data must be pre-processed before applying ML methods.
Ethics and Legislation
- Understanding the role of ethics, regulation and transparency in practical ML applications.
+358 50 555 3900