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Machine Learning and Data Science Blueprints for Finance /
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 stu...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2020.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a 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 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 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. | ||
| 542 | |f Copyright © O'Reilly Media, Inc. | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | |a Online resource; Title from title page (viewed November 25, 2020) | ||
| 505 | 0 | |a Intro -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- How This Book Is Organized -- Part I: The Framework -- Part II: Supervised Learning -- Part III: Unsupervised Learning -- Part IV: Reinforcement Learning and Natural Language Processing -- Conventions Used in This Book -- Using Code Presented in the Book -- Python Libraries -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Special Thanks from Hariom -- Special Thanks from Sahil -- Special Thanks from Brad -- Part I. The Framework -- Chapter 1. Machine Learning in Finance: The Landscape | |
| 505 | 8 | |a Current and Future Machine Learning Applications in Finance -- Algorithmic Trading -- Portfolio Management and Robo-Advisors -- Fraud Detection -- Loans/Credit Card/Insurance Underwriting -- Automation and Chatbots -- Risk Management -- Asset Price Prediction -- Derivative Pricing -- Sentiment Analysis -- Trade Settlement -- Money Laundering -- Machine Learning, Deep Learning, Artificial Intelligence, and Data Science -- Machine Learning Types -- Supervised -- Unsupervised -- Reinforcement Learning -- Natural Language Processing -- Chapter Summary -- Next Steps | |
| 505 | 8 | |a Chapter 2. Developing a Machine Learning Model in Python -- Why Python? -- Python Packages for Machine Learning -- Python and Package Installation -- Steps for Model Development in Python Ecosystem -- Model Development Blueprint -- Chapter Summary -- Next Steps -- Chapter 3. Artificial Neural Networks -- ANNs: Architecture, Training, and Hyperparameters -- Architecture -- Training -- Hyperparameters -- Creating an Artificial Neural Network Model in Python -- Installing Keras and Machine Learning Packages -- Running an ANN Model Faster: GPU and Cloud Services -- Chapter Summary -- Next Steps | |
| 505 | 8 | |a Part II. Supervised Learning -- Chapter 4. Supervised Learning: Models and Concepts -- Supervised Learning Models: An Overview -- Linear Regression (Ordinary Least Squares) -- Regularized Regression -- Logistic Regression -- Support Vector Machine -- K-Nearest Neighbors -- Linear Discriminant Analysis -- Classification and Regression Trees -- Ensemble Models -- ANN-Based Models -- Model Performance -- Overfitting and Underfitting -- Cross Validation -- Evaluation Metrics -- Model Selection -- Factors for Model Selection -- Model Trade-off -- Chapter Summary | |
| 505 | 8 | |a Chapter 5. Supervised Learning: Regression (Including Time Series Models) -- Time Series Models -- Time Series Breakdown -- Autocorrelation and Stationarity -- Traditional Time Series Models (Including the ARIMA Model) -- Deep Learning Approach to Time Series Modeling -- Modifying Time Series Data for Supervised Learning Models -- Case Study 1: Stock Price Prediction -- Blueprint for Using Supervised Learning Models to Predict a Stock Price -- Case Study 2: Derivative Pricing -- Blueprint for Developing a Machine Learning Model for Derivative Pricing | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 700 | 1 | |a Puri, Sahil, |e author. | |
| 700 | 1 | |a Lookabaugh, Brad, |e author. | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
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