You should find the book informative if you are an analyst, data scientist, or ML engineer with an understanding of financial markets and interest in trading strategies. You should also find value as an investment professional who aims to leverage ML to make better decisions.

If your background is software and ML, you may be able to just skim or skip some introductory material on ML. Similarly, if your expertise is in investment, you will likely be familiar with some or all of the financial context. You will likely find the book most useful as a survey of key algorithms, building blocks and use cases than for specialized coverage of a particular algorithm or strategy. However, the book assumes you are interested in continuing to learn about this very dynamic area. To this end, it references numerous resources to support your journey towards customized trading strategies that leverage and build on the fundamental methods and tools it covers.

You should be comfortable using Python 3 and various scientific computing libraries like numpy, pandas, or scipy and be interested in picking up numerous others along the way. Some experience with ML and scikit-learn would be helpful, but we briefly cover the basic workflow and reference various resources to fill gaps or dive deeper.