The series is a comprehensive package containing chapter wise and topic wise guidelines with a vast variety of solved and unsolved exercises to help students practice what they have learnt. These books are strictly in accordance with the latest CBSE syllabus and covers all aspects of formative and summative assessments with the latest marking schemes as laid down by CBSE.

The series is a comprehensive package containing chapter wise and topic wise guidelines with a vast variety of solved and unsolved exercises to help students practice what they have learnt. These books are strictly in accordance with the latest CBSE syllabus and covers all aspects of formative and summative assessments with the latest marking schemes as laid down by CBSE.

The series is a comprehensive package containing chapter wise and topic wise guidelines with a vast variety of solved and unsolved exercises to help students practice what they have learnt. These books are strictly in accordance with the latest CBSE syllabus and covers all aspects of formative and summative assessments with the latest marking schemes as laid down by CBSE.

The series is a comprehensive package containing chapter wise and topic wise guidelines with a vast variety of solved and unsolved exercises to help students practice what they have learnt. These books are strictly in accordance with the latest CBSE syllabus and covers all aspects of formative and summative assessments with the latest marking schemes as laid down by CBSE.

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.

Presents mathematical shortcut and "how to develop a calculator in your head."

In this book an experienced classroom teacher and noted researcher on teaching takes us into her fifth grade math class through the course of a year. Magdalene Lampert shows how classroom dynamics--the complex relationship of teacher, student, and content--are critical in the process of bringing each student to a deeper understanding of mathematics, or any other subject. She offers valuable insights into students and teaching for all who are concerned about improving the learning that happens in the classroom. Lampert considers the teacher's and students' work from many different angles, in views large and small. She analyzes her own practice in a particular classroom, student by student and moment by moment. She also investigates the particular kind of teaching that aims at engaging elementary school students in learning fundamentally important ideas and skills by working on problems. Finally, she looks at the common problems of teaching that occur regardless of the individuals, subject matter, or kinds of practice involved. Lampert arrives at an original model of teaching practice that casts new light on the complexity in teachers' work and on the ways teachers can successfully deal with teaching problems.