A guide to advances in machine learning for financial professionals, with working Python code
Key Features
Explore advances in machine learning and how to put them to work in financial industries
Gain expert insights into how machine learning works, with an emphasis on financial applications
Discover advanced machine learning approaches, including neural networks, GANs, and reinforcement learning
Book DescriptionMachine 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.What you will learn
Apply machine learning to structured data, natural language, photographs, and written text
Understand how machine learning can help you detect fraud, forecast financial trends, analyze customer sentiments, and more
Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow
Delve into neural networks, and examine the uses of GANs and reinforcement learning
Debug machine learning applications and prepare them for launch
Address bias and privacy concerns in machine learning
Who this book is forThis book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
