.MP4, AVC, 380 kbps, 1920x1080 | English, AAC, 144 kbps, 2 Ch | 2h 19m | 648 MB

Instructor: Jakub Konczyk

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.

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

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