▶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 ▶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 ▶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. ▶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. The chapter introduces the Keras functional API, which allows us to build much more complex models that can, for instance, translate between languages. - Chapter 6, Using Generative Models, explains how generative models generate new data. After that, the chapter explains how to monitor and understand the right metrics in production. Finally, it discusses how you can improve the speed of your models. - Chapter 9, Fighting Bias, discusses how ML models can learn unfair policies and even break anti-discrimination laws. It highlights several approaches to improve model fairness, including pivot learning and causal learning. It shows how to inspect models and probe for bias.