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Essential math for data science :take control of your data with fundamental linear algebra, probability, and statistics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Essential math for data science :Thomas Nield.
其他題名:	take control of your data with fundamental linear algebra, probability, and statistics /
作者:	Nield, Thomas
面頁冊數:	xiv, 332 pages :illustrations ;24 cm
附註:	Includes index.
標題:	Machine learningMathematics.
ISBN:	9781098102937

Essential math for data science :take control of your data with fundamental linear algebra, probability, and statistics /
Nield, Thomas(Computer programmer),

Essential math for data science :take control of your data with fundamental linear algebra, probability, and statistics /Thomas Nield. - First edition. - xiv, 332 pages :illustrations ;24 cm

Includes index.

Basic math and calculus review -- Probability -- Descriptive and inferential statistics -- Linear algebra -- Linear regression -- Logistic regression and classification -- Neural networks -- Career advice and the path forward.

To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks --

ISBN: 9781098102937

LCCN: 2023276388

Nat. Bib. No.: GBC290257bnb

Nat. Bib. Agency Control No.: 020621576UkSubjects--Topical Terms:

857106
Machine learning
--Mathematics.Index Terms--Genre/Form:

774816
Handbooks and manuals.

LC Class. No.: QA76.9.D343 / N54 2022

Dewey Class. No.: 006.310151

Essential math for data science :take control of your data with fundamental linear algebra, probability, and statistics /
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