Over the next few decades, a new wave of machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and case studies in supervised, unsupervised, and reinforcement learning. Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, derivative pricing, fraud detection, corporate credit ratings, and robo-advisor and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers:Supervised learning-based regression models for trading strategies, derivative pricing, and risk managementSupervised learning classification-based models such as logistic regression and random forestsTechniques to reduce the number of features in a dataset while retaining useful informationAlgorithms and techniques related to unsupervised learning clustering for finding similar objectsReinforcement learning—a machine learning paradigm with great potential in financeNatural language processing techniques for transforming textual data for use in machine learning algorithms | Machine Learning And Data Science Blueprints For Finance by Hariom Tatsat, Paperback | Indigo Chapters