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Statistical Learning with Math and R: 100 Exercises for Building Logic
Paperback
General ComputersProbability & Statistics
ISBN13: 9789811575679
Publisher: Springer
Published: Oct 20 2020
Pages: 217
Weight: 0.73
Height: 0.49 Width: 6.14 Depth: 9.21
Language: English
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs.
As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning.
This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
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