Over the next few decades, 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 over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, 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 regression-based models for trading strategies, derivative pricing, and portfolio management
Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Part 1. The framework. Machine learning in finance: the landscape
Developing a machine learning model in Python
Artificial neural networks
Part 2. Supervised learning. Supervised learning : models and concepts
Supervised learning : regression (including time series models)
Supervised learning : classification
Part 3. Unsupervised learning. Unsupervised learning : dimensionality reduction
Unsupervised learning : clustering
Part 4. Reinforcement learning and natural language processing. Reinforcement learning
Natural language processing

Over the next few decades, 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 over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, 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 regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations