This series is a multi-skill structured course in English with an intense focus on grammar structure practised through the LSRW skills. Some of the key features of this series include HOTS, Life skills, exercises based on the principle of multiple intelligences and learn-by-doing methods as well as projects and sample test papers to evaluate all skills.

This series is a multi-skill structured course in English with an intense focus on grammar structure practised through the LSRW skills. Some of the key features of this series include HOTS, Life skills, exercises based on the principle of multiple intelligences and learn-by-doing methods as well as projects and sample test papers to evaluate all skills.

This series is a multi-skill structured course in English with an intense focus on grammar structure practised through the LSRW skills. Some of the key features of this series include HOTS, Life skills, exercises based on the principle of multiple intelligences and learn-by-doing methods as well as projects and sample test papers to evaluate all skills.

This series is a multi-skill structured course in English with an intense focus on grammar structure practised through the LSRW skills. Some of the key features of this series include HOTS, Life skills, exercises based on the principle of multiple intelligences and learn-by-doing methods as well as projects and sample test papers to evaluate all skills.

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

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.