Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

By:

Tatsat. Hariom

By:

Publication details: Mumbai: Shroff Publishers & Distributors Pvt. Ltd.,, 2021Description: 409ISBN:

9789385889240

Subject(s):

DDC classification:

006.31 TAT

Summary: 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.

List(s) this item appears in: New Arrivals for the Month of September - 2023

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Alliance College of Engineering and Design	CSE & IT	006.31 TAT (Browse shelf(Opens below))	Available		E12392

Total holds: 0

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Previous	006.31 SRI Machine Learning	006.31 SRI Machine Learning	006.31 STA StatQuest Illustrated Guide to Machine Learning	006.31 TAT Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python	006.31 THE Machine Learning for Absolute Beginners : A Plain English Introduction	006.31 THE Machine Learning for Absolute Beginners : A Plain English Introduction	006.31 THE Machine Learning for Absolute Beginners: A Plain English Introduction	Next

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.

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