The course provides participants with a solid introduction to the field of machine learning, introducing the basic concepts, techniques, and algorithms used in the discipline. The course explores the types of approaches in machine learning, such as supervised, unsupervised and reward learning, as well as the various algorithms used in developing machine learning-based solutions. Participants will learn about data management, the necessary infrastructure, and how to evaluate and implement machine learning solutions in different contexts.
The course is aimed at people who want to familiarize themselves with the basic concepts and techniques in machine learning, without requiring prior experience in the field. This course is suitable for students, IT professionals, data analysts, researchers and anyone interested in understanding and applying machine learning in different contexts. The knowledge acquired in this course will serve as a solid foundation for further exploration of the field of machine learning and artificial intelligence.
After completing this course, participants will acquire knowledge and skills such as:
• Understanding the basic concepts and principles of machine learning, such as supervised, unsupervised and reward learning, and associated approaches and types.
• Familiarity with fundamental algorithms and models in machine learning, such as classification, regression, clustering, and dimensionality reduction, to solve specific problems.
• Understanding the risks and challenges in using machine learning, including issues of bias, model interpretability, and data privacy.
• Managing the data and machine learning infrastructure to train machine learning models and managing the resources needed to effectively implement the machine learning process.
• Choosing and evaluating suitable machine learning models for various problems, data sets and comparing the performance of the models.
This course requires no technical knowledge.
• Introduction to machine learning
• Approaches and types of machine learning: supervised learning, unsupervised learning, reward learning
• Fundamental algorithms in machine learning: classification, regression, clustering, dimensionality reduction
• Data management and infrastructure for machine learning
• Selection and evaluation of models
• Introduction to deep learning
• Practical applications and case studies
• Risks and challenges in using machine learning
The course is not associated with any certification program.
