AI for Finance

AI for Finance

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.

Explore Machine Learning methods to predict future financial events based on past data

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What you will 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

Rating: 3.75

Level: Intermediate Level

Duration: 2.5 hours

Instructor: Packt Publishing