Staying true to its finance ethos, ACCA (the Association of Chartered Certified Accountants) has launched its new course on Certificate in Machine learning with Python for finance professionals. Accountants, finance and business professionals can learn more about machine learning through a new online and on demand course offered by the global professional body for accountants ACCA which has been specifically designed to meet the need for the practical application of these in-demand skills. The course includes 16 practical exercises and solutions to help finance professionals learn how machine learning tools are used to develop time saving solutions.
The certificate’s content is designed for ACCA members and non-members, for accountants, finance and business professionals working in all types of organisations from small businesses to large corporates or financial services. It also provides a formal recognition of learning which can be used both as verifiable CPD for ACCA members, and to demonstrate a new and in-demand skillset to current or future employers.
Md Sajid Khan, Head of International Development at ACCA says: “Artificial Intelligence (AI) is a term that embraces a number of advances including machine learning which uses neural networks, statistics and operational research to identify insights in data without being programmed what to conclude. As the rise of tech continues at pace, it is now a vital part of the skills accountants and other finance professionals need to possess. Machine learning offers time-saving short-cuts which earlier generations could only dream of, and technology frees accountants to fill a more valuable and vital function – as the interpreters of data, not simply its creators.”
This course builds on ‘Machine learning: an introduction for finance professionals’ and assumes a basic understanding of what machine learning (ML) is and how it might be applied. With 20 hours of learning, themes covered include:
- Introduction to Python: initial setup and foundational concepts like data types, variables, mathematical operators, flow control, and functions
- Data analysis: how to load data from different sources, drill down and segment, create pivot table style aggregations and explore data visualisation libraries
- Automating Excel workflows to write macros with 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 to drive adoption and use of Machine Learning
- Understand the basics of a machine learning model and its relationship to data science, Big Data and Artificial Intelligence
- Apply to a real-world machine learning project to meet practical objectives such as evaluating and improving the model, and error detection/correction
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