Machine learning lies at the basis of many recent successes in AI, such as self-driving cars and search engines and now, many industries are discovering its benefits.
This microcredential from Cardiff University will help you understand how machine learning can help businesses in many ways – from reducing costs and improving customer experience to accelerating innovation.
Through guided tutorials, exercises, and labs, you’ll develop key machine learning skills and gain hands-on experience with its applications. With this knowledge, you’ll be able to maximise the potential of machine learning in your workplace.
Throughout the microcredential, you’ll meet representatives from various industries and explore real case studies which will bring the theory to life. This insight will help you finish with a solid understanding of machine learning fundamentals and demonstrable experience of how they can be applied to real-life situations.
Learn to identify and understand a variety of machine learning methods
You’ll delve into the principles underlying common machine learning methods and unpack both traditional methods as well as more recent neural network-based techniques.
With this knowledge, you’ll learn how to implement and evaluate machine learning methods to solve a given task.
Master the fundamentals of data preprocessing
To further your understanding of machine learning methods, you’ll also explore application-oriented aspects, such as how to pre-process data.
This will help you learn how to choose an appropriate machine learning method and data pre-processing strategy to address the needs of a given application setting. You’ll explore this in Python to learn feature engineering, selection, extraction, and dealing with class imbalance and missing data.
Understand how to implement machine learning techniques
To ensure you can apply what you have learned in real-life contexts, you’ll discover how to implement key machine learning techniques and how to choose which technique to use in a given situation.
You’ll also learn how to evaluate the performance of a machine learning system to ensure your processes are optimised.
Explore linear models of machine learning methods
Linear models are a widely used class of machine learning. On this microcredential, you’ll develop your understanding of linear models including support vector machines, decision trees, and ensemble learning.
In addition to these technical topics, you’ll also explore some important ethical considerations, including how the choice of training data can introduce unwanted biases in real-world applications.
With this knowledge, you’ll learn how to eliminate bias and how to assess the ethical implications and societal risks associated with the deployment of machine learning methods.
Improve your data analyst skills alongside industry experts
Guided by the specialists at Cardiff University, you’ll learn crucial data analysis skills to understand the importance of data representation for the success of machine learning models.
You’ll finish the microcredential with both theoretical and practical knowledge of machine learning methods and have the confidence to immediately start using this technology to excel in your career.
How does machine learning improve job opportunities?
Machine learning is a vastly growing field, with most industries having some form of machine learning applications.
As more and more industries adopt machine learning processes, this microcredential can help you develop future-ready skills for an in-demand career in technology.
Will I earn a machine learning qualification?
Yes, you’ll finish with a machine learning qualification from Cardiff University.
How will I be assessed?
You’ll be awarded credits upon passing the final assessment. The assessment will require you to complete a machine learning project on a given data set and will cover the main components of a typical machine learning pipeline. This will include data pre-processing, machine learning method selection and implementation, and performance evaluation.
As part of your assessment, you will also write a concise report (up to 1000 words, excluding tables and figures) to summarise your work and provide an analysis and discussion of the results.
