A new course developed by highly experienced Mathematics educators and textbook writers to cover the MoES Secondary Mathematics syllabus and to prepare students for UCE O-level Matematics examinations. The course consists of four Students' Books, each with an accompanying Teacher's Guide.

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.

This well-established series, the most popular in Nigeria, has been fully revised to reflect recent developments in mathematics education at junior secondary level and the views of the many users of the books. It has expecially been revised to fully cover the requirements of the new NERDC Universal Basic Education Curriculum.

This 2nd edition takes into account recent changes to A-level syllabuses, including the need for modelling. It has been reset to match the larger format of its companion, UNDERSTANDING PURE MATHEMATICS