Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
Other Books You May Enjoy

The machine learning software stack

In this chapter, we will be using a range of different libraries that are commonly used in machine learning. Let's take a minute to look at our stack, which consists of the following software:

  • Keras: A neural network library that can act as a simplified interface to TensorFlow.

  • NumPy: Adds support for large, multidimensional arrays as well as an extensive collection of mathematical functions.

  • Pandas: A library for data manipulation and analysis. It's similar to Microsoft's Excel but in Python, as it offers data structures to handle tables and the tools to manipulate them.

  • Scikit-learn: A machine learning library offering a wide range of algorithms and utilities.

  • TensorFlow: A dataflow programming library that facilitates working with neural networks.

  • Matplotlib: A plotting library.

  • Jupyter: A development environment. All of the code examples in this book are available in Jupyter Notebooks.

The majority of this book is dedicated to working with the Keras...