|
Product 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
Customers Who Bought This Item Also Bought
- Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
- The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
- Practical Time Series Analysis: Prediction with Statistics and Machine Learning
- Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
- Advances in Financial Machine Learning
- Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
- Big Data and Machine Learning in Quantitative Investment (Wiley Finance)
- Machine Learning: An Applied Mathematics Introduction
- Trading Evolved: Anyone can Build Killer Trading Strategies in Python
- Python for Finance: Mastering Data-Driven Finance
*If this is not the "Machine Learning for Finance: Principles and practice for financial insiders" product you were looking for, you can check the other results by clicking this link. Details were last updated on Dec 23, 2024 12:23 +08.