Algorithms Price list in India

A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called "Divide-and-Conquer"), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book * Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. * Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. * Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn * Acquaint yourself with important elements of Machine Learning * Understand the feature selection and feature engineering process * Assess performance and error trade-offs for Linear Regression * Build a data model and understand how it works by using different types of algorithm * Learn to tune the parameters of Support Vector machines * Implement clusters to a dataset * Explore the concept of Natural Processing Language and Recommendation Systems * Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

This text explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. An alternative to the comprehensive algorithm texts in the market, Dasgupta strength is that the math follows the algorithms. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center.

Provides a comprehensive coverage of The subject, includes numerous illustrative example, demonstrate The development of algorithms in a lucid manner, demonstrate The implementation of algorithms in a good programming style, provides challenging programming exercise to test you knowledge gained about The subject, glossary of terms for ready reference table of content1 - review of object-oriented programming concepts - essentials of C++ language - overview of data structures - program design and Development5 - arrays and matrices - linked lists7 - stacks8 - queues9 - trees10 - heaps11 - graphs12 - hash tables and hashing13 - sorting, searching and merging14 - files.

Searching & sorting algorithms form the back bone of coding acumen of developers. This book comprehensively covers

In-depth tutorial & analysis of all major algorithms and techniques used to search and sort across data structures.

All major variations of each algorithm (e.g. Ternary, Jump, Exponential, Interpolation are variations of Binary search).

110 real coding interview questions as solved examples and unsolved problems.

Case studies of implementation of searching and sorting in language libraries.

Introduction to how questions are asked and expected to answer on online competitive coding and hiring platforms like hackerrank.com, codechef.com, etc.

Introduction to data structures.

This textbook is based on the authors' market leading data structures books (in Java and C++) and offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Python has been growing rapidly as the language for CS1, (including increases of 88%, 45% and 49% in the last three years, per TWM.) The lack of texts and lack of consensus in departments have prevented wide adoption in CS2 so far, however, this is changing as more schools see how well Python is working in CS1 and more texts become available.

You are accused of a crime? Who would you rather decides your future – an algorithm or a human? Before making your decision, bear in mind that the algorithm will always be more consistent and far less prone to an error of judgement. Then again, at least the human will be able to look you in the eye before determining your fate. How much fairness would you be willing to sacrifice for that human touch? This is just one of the dilemmas we face in the age of the algorithm, where the machine rules supreme, telling us what to watch, where to go, even who to send to prison. As increasingly we rely on them to automate big, important decisions – in crime, healthcare, transport, money - they raise questions that cut to the heart of what we want our society to look like, forcing us to decide what matters most. Is helping doctors to diagnose patients more or less important than preserving our anonymity? Should we prevent people from becoming victims of crime or protect innocent people from being falsely accused? Hannah Fry takes us on a tour through the good, the bad and the downright ugly of the algorithms that surround us. In Hello World she lifts the lid on their inner workings, demonstrates their power, exposes their limitations and examines whether they really are an improvement on the human systems they replace.

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The sample chapter should give you a very good idea of the quality and style of our book. In particular, be sure you are comfortable with the level and with our Python coding style. This book focuses on giving solutions for complex problems in data structures and algorithm. It even provides multiple solutions for a single problem, thus familiarizing readers with different possible approaches to the same problem.

"Data Structure and Algorithmic Thinking with Python" is designed to give a jump-start to programmers, job hunters and those who are appearing for exams. All the code in this book are written in Python. It contains many programming puzzles that not only encourage analytical thinking, but also prepares readers for interviews. This book, with its focused and practical approach, can help readers quickly pick up the concepts and techniques for developing efficient and effective solutions to problems.

Topics Covered:

• Organization of chapters
• Introduction
• Recursion and Backtracking
• Linked Lists
• Stacks
• Queues
• Trees
• Priority Queue and Heaps
• Disjoint Sets ADT
• Graph Algorithms
• Sorting
• Searching
• Selection Algorithms [Medians]
• Symbol Tables
• Hashing
• String Algorithms
• Algorithms Design Techniques
• Greedy Algorithms
• Divide and Conquer Algorithms
• Dynamic Programming
• Complexity Classes
• Miscellaneous Concepts

This text addresses the often neglected issue of how to actually implement data structures and algorithms. The title "Algorithm Engineering" reflects the authors' approach that designing and implementing algorithms takes more than just the theory of algorithms. It also involves engineering design principles, such as abstract data types, object-orient design patterns, and software use and robustness issues.