Aided by the availability of vast amounts of data computing resources, machine learning (ML) has made big strides. The financial industry, which at its heart is an information processing enterprise, holds an enormous amount of opportunity for the deployment of these new technologies.
This book is a practical guide to modern ML applied in the financial industry. Using a code-first approach, it will teach you how the most useful ML algorithms work, and how to use them to solve real-world problems
There are three kinds of people who would benefit the most from this book:
- Data scientists who want to break into finance and would like to know about the spectrum of possible applications and relevant problems
- Developers in any FinTech business or quantitative finance professionals who look to upgrade their skill set and want to incorporate advanced ML methods into their modeling process
- Students who would like to prepare themselves for the labor market and learn some practical skills valued by employers
This book assumes you have some working knowledge in linear algebra, statistics, probability theory, and calculus. However, you do not have to be an expert in any of those topics.
To follow the code examples, you should be comfortable with Python and the most common data science libraries, such as pandas, NumPy, and Matplotlib. The book's example code is presented in Jupyter Notebooks.
Explicit knowledge of finance is not required.