Binding: Paperback
Description: This lucid accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus linear algebra probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SV Ms boosted trees HM Ms and LD As plus popular deep learning methods such as convolution neural nets attention transformers and G a Ns. Organized in a coherent presentation framework that emphasizes the big picture the text introduces each method clearly and concisely from scratch based on the fundamentals. All methods and algorithms are described by a clean and consistent style with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
Title: Machine Learning Fundamentals A Concise Introduction
Author(s): Jiang Hui (York University Toronto)
Publisher: Cambridge University Press
Barcode: 9781108940023
Pages: 418 Pages, Worked Examples Or Exercises; 19 Halftones, Color; 184 Line Drawings, Color
Publication Date: 3/24/2022
Category: Machine Learning