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Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)
Machine Learning for Factor Investing: Python Version bridges the gap between complex ML concepts and practical investment strategies for both specialists and non-specialists.
Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)
Item #: 254227514

Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)

Item #: 254227514

XAF 81362

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Machine Learning for Factor Investing: Python Version bridges the gap between complex ML concepts and practical investment strategies for both specialists and non-specialists.
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Product Details

Shop Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback) online at a best price in Gabon. 2816026367
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreTextbooks
Publication dateAugust, 2023
Pages340
Reading levelProfessional & Vocational
SubgenreProbability & Statistics
Series titleChapman and Hall/CRC Financial Mathematics
Edition1
PublisherCRC Press
Original languagesEnglish
LanguageEnglish
Edu focusMathematics
Is collectibleN
EditorTony Guida and Guillaume Coqueret
Recording time0 min
Retail packagingSingle Piece
Assembled product dimensions (l x w x h)7.00 x 0.74 x 10.00 in (17.8 x 1.9 x 25.4 cm)
Assembled product weight1.37 lb (620 grams)
Bisac subject headingMathematics

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Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)

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Guillaume Coqueret All Books Editorial Review

Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version is a compelling new addition to the realm of professional textbooks, focusing on the integration of machine learning with factor investing. With 340 pages of content, this paperback edition published in August 2023 is tailored for professionals and vocational readers in the field of Mathematics. It provides in-depth insights into probability and statistics, making it an essential resource for those looking to enhance their quantitative analysis skills in finance. The contributions from editors Tony Guida and Guillaume Coqueret ensure that the material is well-structured and accessible, making the complex subject matter engaging and practical.

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Pros

  • Comprehensive coverage of machine learning techniques
  • Focus on factor investing strategies
  • Written for professional and vocational readers
  • High-quality content from expert editors
  • Useful for enhancing quantitative analysis skills

Cons

  • May be too advanced for beginners in finance

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