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.
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.
The first in the Half-Moon Hollow series is “wry, delicious fun” (Susan Andersen, New York Times bestselling author) as it follows a librarian whose life is turned upside down by a tempestuous and sexy vampire. Maybe it was the Shenanigans gift certificate that put her over the edge. When children’s librarian and self-professed nice girl Jane Jameson is fired by her beastly boss and handed twenty-five dollars in potato skins instead of a severance check, she goes on a bender that’s sure to become Half Moon Hollow legend. On her way home, she’s mistaken for a deer, shot, and left for dead. And thanks to the mysterious stranger she met while chugging neon-colored cocktails, she wakes up with a decidedly unladylike thirst for blood. Jane is now the latest recipient of a gift basket from the Newly Undead Welcoming Committee, and her life-after-lifestyle is taking some getting used to. Her recently deceased favorite aunt is now her ghostly roommate. She has to fake breathing and endure daytime hours to avoid coming out of the coffin to her family. She’s forced to forgo her favorite down-home Southern cooking for bags of O negative. Her relationship with her sexy, mercurial vampire sire keeps running hot and cold. And if all that wasn’t enough, it looks like someone in Half Moon Hollow is trying to frame her for a series of vampire murders. What’s a nice undead girl to do?
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.
