Machine learning & data science blueprints for finance: (Record no. 23940)

MARC details
000 -LEADER
fixed length control field 04433nam a22002417a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789385889240
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number TAT
100 ## - MAIN ENTRY--AUTHOR NAME
Author name Tatsat, Hariom
245 ## - TITLE STATEMENT
Title Machine learning & data science blueprints for finance:
Sub Title from building trading strategies to robo-advisors using python/
Statement of responsibility, etc Hariom Tatsat, Sahil Puri and Brad Lookabaugh.
250 ## - EDITION STATEMENT
Edition statement Grayscale edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication United States of America:
Name of publisher O'Reilly,
Year of publication 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xv, 409 Pg. ;
Other physical details ill. :
Dimensions 21 cm.
500 ## - GENERAL NOTE
General note 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).<br/><br/>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.<br/><br/>This book covers:<br/><br/>Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management<br/>Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies<br/>Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction<br/>Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management<br/>Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management<br/>NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Part 1. The framework. Machine learning in finance: the landscape<br/>Developing a machine learning model in Python<br/>Artificial neural networks<br/>Part 2. Supervised learning. Supervised learning : models and concepts<br/>Supervised learning : regression (including time series models)<br/>Supervised learning : classification<br/>Part 3. Unsupervised learning. Unsupervised learning : dimensionality reduction<br/>Unsupervised learning : clustering<br/>Part 4. Reinforcement learning and natural language processing. Reinforcement learning<br/>Natural language processing
520 ## - SUMMARY, ETC.
Summary, etc 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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Natural language processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Machine Learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Python
700 ## - ADDED ENTRY--PERSONAL NAME
Author 2/ Editor Puri, Sahil
700 ## - ADDED ENTRY--PERSONAL NAME
Author 2/ Editor Lookabaugh, Brad
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Bill Date Full call number Accession Number Price effective from Koha item type
    Dewey Decimal Classification     Institute of Public Enterprise, Library Institute of Public Enterprise, Library S Campus 08/05/2025 Professional Book Services 1700.00 08-07-2025 006.31 TAT 50534 08/05/2025 Books

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