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...

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Detalles Bibliográficos
Autores principales: Tatsat, Hariom (Autor), Puri, Sahil (Autor), Lookabaugh, Brad (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: O'Reilly Media, Inc., 2020.
Edición:1st edition.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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