Sprache: Englisch
Verlag: Universities Press (India) Pvt. Ltd., 2023
ISBN 10: 9393330344 ISBN 13: 9789393330345
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In den WarenkorbPaperback. Zustand: Brand New. 122 pages. 9.00x6.00x0.31 inches. In Stock.
Sprache: Englisch
Verlag: Universities Press (India) Pvt. Ltd., 2020
ISBN 10: 9389211565 ISBN 13: 9789389211566
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Verlag: Twenty-First Century Publications
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Soft cover. Zustand: New. R is a free, open-source, adaptable, extensible language with tremendous applications in the field of statistical computation and data science. It offers basic programming and has very strong built-in functions for statistical analysis. It is also a perfect fit for big data solutions and supports graphical techniques to visualise and present data effectively. In this book, students will learn about R programming, from its fundamentals to advanced concepts relating to data science and machine learning.
Soft cover. Zustand: New. Contents: Preface. 1. Introduction to Data Science. 2. Environment Set-up and Basics of Python. 3. NumPy and pandas. 4. Data Visualization. 5. Python scikit-learn. 6. Environment Set-up: TensorFlow and Keras. 7. Probability. 8. Machine Learning and Data Pre-processing. 9. Statistical Analysis: Descriptive Statistics. 10. Statistical Analysis: Inferential Statistics. 11. Classification. 12. Prescriptive Analytics: Data Stream Mining. 13. Language Data Processing in Python. 14. Clustering. 15. Association Rule Mining. 16. Time Series Analysis Using Python. 17. Deep Neural Network and Convolutional Neural Network. 18. Case Studies. Index. Data Science and Machine Learning are the leading buzzwords of today. This book covers all aspects of these subjects, from data definition and categorization, classification techniques, clustering and ML algorithms to data stream and association rule mining, language data processing and neural networks. It explains descriptive and inferential statistical analysis, probability distribution and density functions as well as time series. It also describes the fundamentals of Python programming, the Python environment and libraries such as scikit-learn, NumPy and pandas, and takes a deep dive into data visualization modules and tools. Mastery of these areas will enable students to become proficient and effective data scientists. Salient features Ideal for undergraduate courses on Data Science and Analytics Provides step-by-step instructions for setting up the Python environment and executing various libraries and packages All chapters include relevant case studies, their Python code and output; the last chapter is dedicated to case studies Over 300 exercise questions comprising MCQs, programming exercises and concept-based questions, with answers provided for quick reference Bibliography at the end of every chapter for further reading Android app with chapter-wise PowerPoint slides and job interview questions.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Nov 2012, 2012
ISBN 10: 3659278416 ISBN 13: 9783659278419
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Taschenbuch. Zustand: Neu. Neuware -Handwriting recognition is a difficult problem, because of the great amount of variations in human handwriting. When observed in isolation also, characters are often ambiguous. We have implemented some soft computing techniques using structural and statistical feature sets for constrained handwritten isolated Devnagari characters and numerals. Some preprocessing steps are applied before extracting statistical and structural information of character image. As conventional histogram based method does not work for handwritten Devnagari characters, differential distance based technique is designed to find shirorekha and spine. Multilayer perceptron, support vector machines and edit distance classifiers are used for classification. Three MLP combination techniques namely: max, min and weighted majority scheme is applied. Two approaches for two stage classification is discussed in detail to improve the accuracy. As there are many similar shaped characters, character set is grouped in two sets-- certainty and confused character set. Relative difference measure is used for grouping of character sets. Each set is classified using different classifier.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch.
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