This is the code repository for AI for Finance [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
A lot of solutions to key problems in 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 like financial forecasting.
In this course you will 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 will learn how to choose the basic data preparation method and model and then how to improve them. In the next module you will discover a variety of ways to prepare data and then how they influence models training accuracy. In the last module you will 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 will have a solid introduction to apply ML methods to financial data forecasting.
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Python 3 working knowledge
Basic shell skills - how to run a simple command from Terminal
We will cover the basics of working with ML models with financial data so you will be encouraged to digging deeper for more details
This course has the following software requirements:
This course has the following software requirements:
Conda package manager with Python3.7 (https://conda.io/en/master/miniconda.html )
A code editor, author used Atom in the course
This course has been tested on the following system configuration:
OS: macOS High Sierra
Processor: 1,3 GHz Intel Core 5
Memory: 4 GB
Storage: 121 GB
