Machine Learning Fundamentals: A Practical Introduction with Python
While many IT professionals and citizens associate Artificial Intelligence primarily with Generative AI and Agentic AI, a wide range of valuable AI applications still rely on traditional Machine Learning techniques. This course introduces participants to the fundamental principles of Machine Learning and provides deeper insights into practical Machine Learning algorithms through real-world use cases. Participants will learn how these techniques can be applied to solve concrete business and operational challenges. The course combines immediate practical value with a strong foundation for understanding more advanced AI concepts, including Deep Learning, Generative AI, Foundation Models and Large Language Models. Hands-on exercises are performed using Python. However, advanced Python programming skills are not required, as the focus is on understanding Machine Learning concepts and applying them effectively.
What you will learn
In this course, you will build a solid understanding of Machine Learning and its role within the broader Artificial Intelligence landscape. In this course, you will build a solid understanding of Machine Learning and its role within the broader Artificial Intelligence landscape. You will learn:
- The different flavours of Artificial Intelligence, including Machine Learning, Deep Learning, Foundation Models, Large Language Models (LLMs), and Generative AI.
- The role of Python in Data Science and Machine Learning, including the essential concepts and tools used by practitioners.
- How to apply fundamental Machine Learning algorithms to practical, real-world use cases.
- How to train, evaluate, and improve Machine Learning models.
- How to measure the quality and reliability of predictions and classifications using appropriate evaluation techniques.
- How to identify situations where Machine Learning can provide valuable solutions in professional and personal contexts.
- How to apply Machine Learning techniques responsibly to achieve reliable and meaningful results.
Programme
- Foundations of Machine Learning
- The role of data, features, models, training, and evaluation
- Introduction or refresh of the essential Python concepts used in Data Science
- Working with the Python Libraries for Machine Learning practitioners
- Regression Technques
- Quality Measures for Regression
- Classification Techniqus
- Quality Measures for Classificatoon
- Extensive Hands-On Machine Learning with Python