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A number of national level examinations like IAS Mains, State PCS, IES, IFS, etc. check the English language aptitude of candidates with a compulsory English language paper. This book has been designed for such examinations which assess the English language abilities in detail.The present book covering English Language in descriptive nature has been divided into four parts namely Writing Skills, Comprehension Skills, Grammar Skills & Verbal Skills.Part A has been further divide into Essay Writing, Expansion Writing, Report Writing, Precis Writing and Letter Writing. The Verbal Skills section covers Paronyms & Homonyms, One Word Substitutions, Synonyms & Antonyms, Phrasel Verbs, etc. whereas the Grammar Skills section covers The Function of Tenses, Voice, Narration, Synthesis, Prepositions, etc. Each chapter in the book contains detailed theory including a number of sample examples in a simple & easier way to understand the topics well. Workbook Exercises have been provided in between the chapters for Part B, C & D to help aspirants practice the concepts discussed in each chapter. Answers for all the questions have been given at the end of the section. As the book covers varied aspects of English language, it for sure will help aspirants score high in the upcoming national and state level competitive and recruitment examinations.
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Elephants are possibly the most well-known members of the animal kingdom. The enormous size, unusual anatomy, and longevity of elephants have fascinated humans for millenia. Biology, Medicine, and Surgery of Elephants serves as a comprehensive text on elephant medicine and surgery. Based on the expertise of 36 scientists and clinical veterinarians, this volume covers biology, husbandry, veterinary medicine and surgery of the elephant as known today. Written by the foremost experts in the field Comprehensively covers both Asian and African elephants Complete with taxonomy, behavioral, geographical and systemic information Well-illustrated and organized for easy reference
Hard-headed evidence on why the returns from investing in girls are so high that no nation or family can afford not to educate their girls. Gene Sperling, author of the seminal 2004 report published by the Council on Foreign Relations, and Rebecca Winthrop, director of the Center for Universal Education, have written this definitive book on the importance of girls’ education. As Malala Yousafzai expresses in her foreword, the idea that any child could be denied an education due to poverty, custom, the law, or terrorist threats is just wrong and unimaginable. More than 1,000 studies have provided evidence that high-quality girls’ education around the world leads to wide-ranging returns: Better outcomes in economic areas of growth and incomes Reduced rates of infant and maternal mortality Reduced rates of child marriage Reduced rates of the incidence of HIV/AIDS and malaria Increased agricultural productivity Increased resilience to natural disasters Women’s empowerment What Works in Girls’ Education is a compelling work for both concerned global citizens, and any academic, expert, nongovernmental organization (NGO) staff member, policymaker, or journalist seeking to dive into the evidence and policies on girls’ education.
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.