Author: Jan BellPublisher:
ISBN: 9781292202266
Category :
Languages : en
Pages : 224
Book Description
Author: Jan BellExam Maximiser
Author: Jacky NewbrookPublisher: Pearson Longman
ISBN: 9781292202235
Category : Juvenile Nonfiction
Languages : en
Pages : 128
Book Description
Gold Exam Maximisers provide extensive support for the coursebook with language work, additional practice exam tasks and extras. The Exam Maximiser can be used alongside the coursebook or on its own for re-takers. Comprehensive revision, practice and extension, and additional Use of English sections Additional practice of skills, exam tasks and language points A complete practice test
Gold B2 First New 2018 Edition Exam Maximiser with Key
Author: Sally BurgessPublisher: Pearson Longman
ISBN: 9781292202242
Category : Juvenile Nonfiction
Languages : en
Pages : 144
Book Description
Gold Exam Maximisers provide extensive support for the coursebook with language work, additional practice exam tasks and extras. The Exam Maximiser can be used alongside the coursebook or on its own for re-takers. Comprehensive revision, practice and extension, and additional Use of English sections Additional practice of skills, exam tasks and language points A complete practice test
Author: Clementine Annabell
Author: Jacky Newbrook
Author: Ready for Fce
Author: Roy NorrisPublisher: MacMillan
ISBN: 9780230440111
Category :
Languages : en
Pages : 168
Book Description
The Teacher's Book is accompanied by a DVD-ROM with tests in both PDF and editable Word format, along with a series of teacher training videos. There is also additional photocopiable material in the Teacher's Book, with a focus on material for the Speaking test.
Author: Mathematics for Machine Learning
Author: Marc Peter DeisenrothPublisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
