AI for Finance
What you'll learn
- Get hands-on financial forecasting experience using machine learning with Python, Keras, Scikit-Learn and pandas
- Use a variety of data preparation methods with financial data
- Predict future values based on single and multiple values
- Apply key modern Machine Learning methods for forecasting
- Understand the process behind choosing the best performing data preparation method and model
- Grasp Machine Learning forecasting on a specific real-world financial data
Requirements
- Some basic knowledge related to Python is assumed. However, no knowledge about financial data analysis is assumed.
Description
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.
About The Author
Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra.
Who this course is for:
- This course is for aspiring data scientists, ML practitioners, as well as Investment Analysts and Portfolio managers working in the finance and investment industry.
Instructor
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