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Machine Learning and Data Science Blueprints for Finance - by  Hariom Tatsat & Sahil Puri & Brad Lookabaugh (Paperback) - 1 of 1

Machine Learning and Data Science Blueprints for Finance - by Hariom Tatsat & Sahil Puri & Brad Lookabaugh (Paperback)

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About this item

Highlights

  • Over the next few decades, machine learning and data science will transform the finance industry.
  • About the Author: Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York.
  • 429 Pages
  • Computers + Internet, Programming Languages

Description



About the Book



Machine learning and data science will significantly transform the finance industry in the next few years. With this practical guide, professionals at hedge funds, investment and retail banks, and fintech firms will learn how to build ML algorithms crucial to this industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).



Book Synopsis



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



About the Author



Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley and his BE at IIT Kharagpur (India). Hariom has also completed FRM (Financial Risk Manager), CQF (Certificate in Quantitative Finance) and is a candidate for CFA Level 3.

Sahil Puri works as a Quantitative Researcher in the Analytics Division at P.I.M.C.O. His work involves testing model assumptions and finding strategies for multiple asset classes. Sahil has applied multiple statistical and machine learning based techniques to a wide variety of problems; examples include: generating text features, labeling curve anomalies, non-linear risk factor detection, and time series prediction. He completed his MS at UC Berkeley and his BE at Delhi College of Engineering (India).

Dimensions (Overall): 9.1 Inches (H) x 7.0 Inches (W) x 1.0 Inches (D)
Weight: 1.45 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 429
Genre: Computers + Internet
Sub-Genre: Programming Languages
Publisher: O'Reilly Media
Theme: Python
Format: Paperback
Author: Hariom Tatsat & Sahil Puri & Brad Lookabaugh
Language: English
Street Date: December 15, 2020
TCIN: 83300503
UPC: 9781492073055
Item Number (DPCI): 247-56-8145
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 1 inches length x 7 inches width x 9.1 inches height
Estimated ship weight: 1.45 pounds
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Q: Who are the authors of this book?

submitted by AI Shopping Assistant - 2 months ago
  • A: The authors are Hariom Tatsat, Sahil Puri, and Brad Lookabaugh, each with a background in quantitative analytics.

    submitted byAI Shopping Assistant - 2 months ago
    Ai generated

Q: What types of learning methods are discussed?

submitted by AI Shopping Assistant - 2 months ago
  • A: The book discusses supervised, unsupervised, and reinforcement learning methods, along with NLP techniques.

    submitted byAI Shopping Assistant - 2 months ago
    Ai generated

Q: What is the target audience for this book?

submitted by AI Shopping Assistant - 2 months ago
  • A: This book is ideal for professionals in hedge funds, investment banks, and fintech firms.

    submitted byAI Shopping Assistant - 2 months ago
    Ai generated

Q: What key topics are covered in this book?

submitted by AI Shopping Assistant - 2 months ago
  • A: The book covers machine learning concepts, case studies in various learning methods, portfolio management, algorithmic trading, and NLP techniques.

    submitted byAI Shopping Assistant - 2 months ago
    Ai generated

Q: What practical applications does the book emphasize?

submitted by AI Shopping Assistant - 2 months ago
  • A: It emphasizes applications in trading strategies, fraud detection, portfolio management, and sentiment analysis.

    submitted byAI Shopping Assistant - 2 months ago
    Ai generated

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2.0 out of 5 stars with 1 reviews
2 out of 5 stars
25 November, 2021Verified purchase

Meh

I couldn't get myself to keep reading this book. Slightly outdated and messy format.
A
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