Machine Learning Fundamentals

Course description

The course provides participants with a solid introduction to the field of machine learning, presenting basic concepts, techniques and algorithms used in this 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 required infrastructure and how to evaluate and implement machine learning solutions in different contexts.

What will you learn

After completing this course, participants will gain knowledge and skills such as:

• Understand the basic concepts and principles of machine learning, such as supervised, unsupervised and reward learning, and the associated approaches and types.
• Become familiar with fundamental algorithms and models in machine learning, such as classification, regression, clustering and dimensionality reduction, to solve specific problems.
• Understand the risks and challenges in using machine learning, including issues of bias, model interpretability, and data privacy.
• Managing the data and infrastructure for machine learning to train machine learning models and managing the resources needed to effectively implement machine learning.
• Choosing and evaluating appropriate machine learning models for various problems, datasets and comparing model performance.

Target audience

The course is aimed at people who want to familiarize themselves with the basic concepts and techniques of 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 gained in this course will serve as a solid foundation for further study in the field of machine learning and artificial intelligence.

Prerequisites:

This course does not require technical knowledge.

Course module structure

• Introduction to machine learning
• Machine learning approaches and types: supervised learning, unsupervised learning, reward learning
• Fundamental algorithms in machine learning: classification, regression, clustering, dimensionality reduction
• Data management and infrastructure for machine learning
• Choice and evaluation of models
• Introduction to deep learning
• Practical applications and case studies
• Risks and challenges in using machine learning