DESCRIPTION


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
Clear explanation and expert discussion of how machine learning works, with an emphasis on financial applications
Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learning
Book Description
Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It 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 the advanced ML concepts and ideas that can be applied in a wide variety of ways.
The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and 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
How machine learning can 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
Dig deep into neural networks, examine 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 for
This 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.
Table of Contents
Neural Networks and Gradient-Based Optimization
Applying Machine Learning to Structured Data
Utilizing Computer Vision
Understanding Time Series
Parsing Textual Data with Natural Language Processing
Using Generative Models
Reinforcement Learning for Financial Markets
Privacy, Debugging, and Launching Your Products
Fighting Bias
Bayesian Inference and Probabilistic Programming