And thus, we close the last chapter of our journey, and I say goodbye to you, dear reader. Let's look back at the table of contents that we were met with at the start of our journey.
Over the past 10 chapters, we've covered a whole lot, including the following:
- Gradient descent-based optimization
- Feature engineering
- Tree-based methods
- Computer vision
- Time series models
- Natural language processing
- Generative models
- Debugging machine learning systems
- Ethics in machine learning
- Bayesian inference
In each chapter, we created a large bag of practical tips and tricks that you can use. This will allow you to build state-of-the-art systems that will change the financial industry.
Yet, in many ways we have only scratched the surface. Each of the chapter topics merit their own book, and even that would not adequately cover everything that could be said about machine learning in finance.
I leave you with this thought: Machine learning in finance is an exciting field in which there is still much to uncover, so onward dear reader; there are models to be trained, data to be analyzed, and inferences to be made!