Machine Learning for Finance

What this book covers

Chapter 1, Neural Networks and Gradient-Based Optimization, will explore what kinds of ML there are, and the motivations for using them in different areas of the financial industry. We will then learn how neural networks work and build one from scratch.

Chapter 2, Applying Machine Learning to Structured Data, will deal with data that resides in a fixed field within, for example, a relational database. We will walk through the process of model creation: from forming a heuristic, to building a simple model on engineered features, to a fully learned solution. On the way, we will learn about how to evaluate our models with scikit-learn, how to train tree-based methods such as random forests, and how to use Keras to build a neural ...