Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics. Tony Guida is executive director at RAM Active Investments. He serves as chair of the machineByte think tank and is the author of Big Data and Machine Learning in Quantitative Investment.

"This book is the perfect one for any data scientist on financial markets. It is well written, with lots of illustrations, examples, pieces of code, tips on the different statistical package available to perform the various algos. This book requires for sure a strong knowledge in quantitative finance and Machine Learning, so it cannot be put in any hands. But for those who are familiar with quantitative finance, this book can be a reference, as Hull's book is as regards to derivatives products. I liked the good and detailed analysis of the different Machine Learning algos, and the different examples used throughout the book. This book is perfect for assets managers having to run backtests and searching for innovative ways to enhance the return of their portfolios. I spent quite a good time reading this manuscript, and I would recommend it." (Frédéric Girod, Union of European Football Associations)

• 1. Preface 2. Notations and data 3. Introduction 4. Factor investing and asset pricing anomalies 5. Data preprocessing 6. Penalized regressions and sparse hedging for minimum variance portfolios 8. Neural networks 7. Tree-based methods 9. Support vector machines 10. Bayesian methods 11. Validating and tuning 12. Ensemble models 13. Portfolio backtesting 14. Interpretability 15. Two key concepts: causality and non-stationarity 16. Unsupervised learning 17. Reinforcement learning

Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics. Tony Guida is executive director at RAM Active Investments. He serves as chair of the machineByte think tank and is the author of Big Data and Machine Learning in Quantitative Investment.

"This book is the perfect one for any data scientist on financial markets. It is well written, with lots of illustrations, examples, pieces of code, tips on the different statistical package available to perform the various algos. This book requires for sure a strong knowledge in quantitative finance and Machine Learning, so it cannot be put in any hands. But for those who are familiar with quantitative finance, this book can be a reference, as Hull's book is as regards to derivatives products. I liked the good and detailed analysis of the different Machine Learning algos, and the different examples used throughout the book. This book is perfect for assets managers having to run backtests and searching for innovative ways to enhance the return of their portfolios. I spent quite a good time reading this manuscript, and I would recommend it." (Frédéric Girod, Union of European Football Associations)

• 1. Preface 2. Notations and data 3. Introduction 4. Factor investing and asset pricing anomalies 5. Data preprocessing 6. Penalized regressions and sparse hedging for minimum variance portfolios 8. Neural networks 7. Tree-based methods 9. Support vector machines 10. Bayesian methods 11. Validating and tuning 12. Ensemble models 13. Portfolio backtesting 14. Interpretability 15. Two key concepts: causality and non-stationarity 16. Unsupervised learning 17. Reinforcement learning