Machine learning with Python for finance professionals

This course is a part of FinTech for Finance and Business Leaders, a 3-course Professional Certificate series from edX.

Welcome to Machine learning with Python for finance professionals, provided by ACCA (Association of Chartered Certified Accountants), the global body for professional accountants. This course is part of the FinTech for finance and business leaders professional certificate program.

This course will provide a view of what lies under the surface of a machine learning output, help to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning. Digital finance knowledge and skills are essential components of the technology transformation as business becomes increasingly customer focused. And having the skills to understand how these technologies are deployed and integrated into a customer centric business strategy is essential. With 16 Jupyter Notebooks available, alongside corresponding solution notebooks, and bonus exercises you will quickly become skilled in specific time-saving Machine Learning tools

Access to all end of module quizzes

Access to the final assessment

What you'll learn

• An introduction to Python starting from initial setup and explaining foundational concepts like data types, variables, mathematical operators, flow control, and functions
• Using Python for data analysis including how to load data from different sources, drill down and segment, create pivot table style aggregations and explore data visualisation libraries.
• Automating Excel workflows using Python to write macros that can be run at the click of a button using the full power of the Python eco-system; and to create template reports that update live with the latest data.
• How to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of Machine Learning
• Understand the basic workings of a machine learning model and its relationship to data science, Big Data and Artificial Intelligence.
• Apply to real-world machine learning examples to meet practical objectives such as evaluating and improving the model, and error detection/correction.