A lot of solutions to key problems in the financial world require predicting the future patterns in data from the past to make better financial decisions right now. The evolution of modern machine learning methods and tools in recent years in the field of computer vision bring promise of the same progress in other important fields such as financial forecasting.
In this course, you’ll first learn how to quickly get started with ML in finances by predicting the future currency exchange rates using a simple modern machine learning method. In this example, you’ll learn how to choose the basic data preparation method and model and then how to improve them. In the next module, you’ll discover a variety of ways to prepare data and then see how they influence models training accuracy. In the last module, you’ll learn how to find and test a few key modern machine learning models to pick up the best performing one.
After finishing this course, you’ll have a solid introduction to apply ML methods to financial data forecasting.
Target Audience
This course is for aspiring data scientists, ML practitioners, as well as Investment Analysts and Portfolio managers working in the finance and investment industry. Some basic knowledge related to Python is assumed. However, no knowledge about the financial data analysis is assumed.
Business Outcomes
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Learn the key Machine Learning (ML) techniques commonly used for Financial forecasting: from a simple Machine Learning model to using more complex ones
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Explore tools such as pandas, Scikit-Learn, Keras, and Tensorflow for applications in Finance
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Get Hands-on training to prepare financial data for analysis and use it to make future value predictions
