Introduction to Applied Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools used in a wide variety of areas such as finance, healthcare, e-commerce, industry, entertainment and many others to automate numerous tasks. For example, in healthcare an ML algorithm can be used to predict a diagnosis based on the patient’s data. An autonomous vehicle can employ an AI algorithm to decide whether to apply emergency breaking. Fraud and money laundering can also be successfully identified by AI and ML technology when used by a financial institution. Successful deployment of this technology requires understanding of application areas of different AI and ML algorithms and their performance characteristics. The course introduces basic skills required to design and build simple machine learning solutions using Python as the programming language and the relevant Python libraries. In particular, the course focuses on both supervised and unsupervised machine learning including such algorithms as Linear Regression, Support Vector Machines, Decision Trees, Ensemble Learning, K-Means, and DBSCAN. During the course, the students will learn to select the right ML algorithm for the task and optimize its parameters in order to achieve desired performance. The target group for the course is senior and medium level management as well as engineering and technical personnel unfamiliar with AI and ML technology. ## Industry and academy This course has been developed in collaboration with industry partners within the framework of one of our educational projects. The project aims to provide courses at the advanced level to a mix of professionals and students to strengthen the Swedish industry's competitiveness. We apply validation of work experience to see if you have knowledge that corresponds to the eligibility requirements for the course. Do not forget to attach a description if you are applying based on work experience, a form is available on our website [www.hv.se/produktionskurser](<https://www.hv.se/produktionskurser>). ## SCHEDULE From the start of the course, the updated schedule will apply on the course's Canvas page.