Machine Learning and Data Science Blueprints for Finance

Machine Learning and Data Science Blueprints for Finance
PDF, ePUB
  • eBook:
    Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python
  • Author:
    Hariom Tatsat, Sahil Puri, Brad Lookabaugh
  • Edition:
    1 edition
  • Categories:
  • Data:
    November 10, 2020
  • ISBN:
    1492073059
  • ISBN-13:
    9781492073055
  • Language:
    English
  • Pages:
    432 pages
  • Format:
    PDF, ePUB

Book Description
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

Content

I. The Framework
1. Machine Learning in Finance: The Landscape
2. Developing a Machine Learning Model in Python
3. Artificial Neural Networks

II. Supervised Learning
4. Supervised Learning: Models and Concepts
5. Supervised Learning: Regression (Including Time Series Models)
6. Supervised Learning: Classification

III. Unsupervised Learning
7. Unsupervised Learning: Dimensionality Reduction
8. Unsupervised Learning: Clustering

IV. Reinforcement Learning and Natural Language Processing
9. Reinforcement Learning
10. Natural Language Processing

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