Me 'n' Mine is a term course comprising 15 books for grades 1 to 5, 3 books per grade, spread over 3 terms. The core subjects covered are English, Maths, EVS/Science and Social Studies. The contents are broadly derived from the guidelines provided in NCF 2022 and NEP 2020. The books focus on providing quality education while reducing the extra burden on students. They embed the principles and practices of hands-on, and responsive teaching and learning while focusing on the common goal of improving education. Its myriad innovative, creative and interactive features make teaching and learning participative and interesting.

Me 'n' Mine is a term course comprising 15 books for grades 1 to 5, 3 books per grade, spread over 3 terms. The core subjects covered are English, Maths, EVS/Science and Social Studies. The contents are broadly derived from the guidelines provided in NCF 2022 and NEP 2020. The books focus on providing quality education while reducing the extra burden on students. They embed the principles and practices of hands-on, and responsive teaching and learning while focusing on the common goal of improving education. Its myriad innovative, creative and interactive features make teaching and learning participative and interesting.

Me 'n' Mine is a term course comprising 15 books for grades 1 to 5, 3 books per grade, spread over 3 terms. The core subjects covered are English, Maths, EVS/Science and Social Studies. The contents are broadly derived from the guidelines provided in NCF 2022 and NEP 2020. The books focus on providing quality education while reducing the extra burden on students. They embed the principles and practices of hands-on, and responsive teaching and learning while focusing on the common goal of improving education. Its myriad innovative, creative and interactive features make teaching and learning participative and interesting.

A text book on science

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.