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An elementary approach to design and analysis of algorithms

Authors: Lekh Raj Vermani (2019)

This book introduces a set of concepts in solving problems computationally such as Growth of Functions; Backtracking; Divide and Conquer; Greedy Algorithms; Dynamic Programming; Elementary Graph Algorithms; Minimal Spanning Tree; Single-Source Shortest Paths; All Pairs Shortest Paths; Flow Networks; Polynomial Multiplication, to ways of solving NP-Complete Problems, supported with comprehensive, and detailed problems and solutions, making it an ideal resource to those studying computer science, computer engineering and information technology.

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Quantum Computing: An Applied Approach

Authors: Jack D. Hidary (2019)

The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more

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Hands-On Data Science for Marketing : Improve your marketing strategies with machine learning using Python and R

Authors: Yoon Hyup Hwang (2019)

This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies.

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Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems

Authors: Yuxi (Hayden) Liu (2019)

Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.

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