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Auteur Harsh BHASIN |
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Data Structures with Python : Get familiar with the common Data Structures and Algorithms in Python (English Edition) / Harsh BHASIN / BPB Publications (2023)
Titre : Data Structures with Python : Get familiar with the common Data Structures and Algorithms in Python (English Edition) Type de document : e-book Auteurs : Harsh BHASIN Editeur : BPB Publications Année de publication : 2023 ISBN/ISSN/EAN : 9789355513304 Note générale : copyrighted Langues : Anglais (eng) Résumé : Develop a strong foundation in Data Structures and Algorithms and become a skilled programmer Key Features ? Explore various data structures and algorithms and their applications. ? Learn how to use advanced data structures and algorithms to solve complex computational problems. ? An easy-to-understand guide that gives a comprehensive introduction to data structures and algorithms using the Python programming language. Description Data structures are a way of organizing and storing data in a computer so that it can be accessed and manipulated efficiently. If you want to become an accomplished programmer and master this subject, then this book is for you. The book starts by introducing you to the fascinating world of data structures and algorithms. This book will help you learn about different algorithmic techniques such as Dynamic programming, Greedy algorithms, and Backtracking, and their applications in solving various computational problems. The book will then teach you how to analyze the complexity of Recursive algorithms. Moving on, the book will help you get familiar with the concept of Linked lists, which is an important foundation for understanding other data structures, such as Stacks and Queues, which are covered in detail later in this book. The book will also teach you about advanced data structures such as Trees and Graphs, their different types, and their applications. Towards the end, the book will teach you how to use various Sorting, Searching Selection and String algorithms. By the end of the book, you will get a comprehensive and in-depth understanding of various data structures and algorithms and their applications in solving real-world computational problems efficiently. What you will learn ? Get familiar with the fundamentals of data structures such as arrays, linked lists, stacks, and queues. ? Understand the basics of algorithm analysis and complexity theory. ? Explore different approaches to the algorithm design, such as divide-and-conquer, dynamic programming, and greedy algorithms. ? Work with common data structures such as arrays, linked lists, stacks, queues, trees, heaps, and graphs. ? Discover sorting and searching algorithms, including hash tables and string algorithms. Who this book is for The book is aimed at Computer Science students, Software Engineers, and anyone interested in learning about data structures and algorithms Table of Contents 1. Introduction to Data Structures 2. Design Methodologies 3. Recursion 4. Arrays 5. Linked List 6. Stacks 7. Queues 8. Trees-I 9. Trees-II 10. Priority Queues 11. Graphs 12. Sorting 13. Median and Order Statistics 14. Hashing 15. String Matching Appendix 1: All Pairs Shortest Path Appendix 2: Tree Traversals Appendix 3: Dijkstra’s Shortest Path Algorithm Appendix 4: Supplementary Questions Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88941405 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=572964 Machine Learning for Beginners : Learn to Build Machine Learning Systems Using Python (English Edition) / Harsh BHASIN / BPB Publications (2020)
Titre : Machine Learning for Beginners : Learn to Build Machine Learning Systems Using Python (English Edition) Type de document : e-book Auteurs : Harsh BHASIN Editeur : BPB Publications Année de publication : 2020 ISBN/ISSN/EAN : 9789389845426 Note générale : copyrighted Langues : Anglais (eng) Résumé : Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key FeaturesUnderstand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work.Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learnLearn how to prepare Data for Machine Learning.Learn how to implement learning algorithms from scratch.Use scikit-learn to implement algorithms.Use various Feature Selection and Feature Extraction methods.Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning. Your LinkedIn Profile:https://in.linkedin.com/in/harsh-bhasin-69134426 Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88939136 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=564514
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