Thinking Data Science

A Data Science Practitioner’s Guide

AvPoornachandra Sarang

Häftad, Engelska, 2024

Del i serien Springer Series in Applied Machine Learning

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Beskrivning

Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

Produktinformation

Utgivningsdatum:2024-03-02
Mått:155 x 235 x 21 mm
Vikt:575 g
Format:Häftad
Språk:Engelska
Serie:Springer Series in Applied Machine Learning
Antal sidor:358
Förlag:Springer International Publishing AG
ISBN:9783031023651

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Poornachandra Sarang, in his IT career spanning four decades, has been consulting large IT organizations on the design and architecture of systems using state-of-the-art technologies. He has authored several books covering a wide range of emerging technologies. Dr. Sarang is a Ph.D. advisor for Computer Science and Engineering and is on the thesis advisory committee for aspiring doctoral candidates. He has designed and delivered courses/curricula for universities at the postgraduate level, including courses and workshops on emerging technologies for industry. He is a known face at technical and research conferences delivering both keynote and technical talks.

Innehållsförteckning

Chapter. 1. Data Science Process.- Chapter. 2. Dimensionality Reduction - Creating Manageable Training Datasets.- Chapter. 3. Classical Algorithms - Over-view.- Chapter. 4. Regression Analysis.- Chapter. 5. Decision Tree.- Chapter. 6. Ensemble - Bagging and Boosting.- Chapter. 7. K-Nearest Neighbors.- Chapter. 8. Naive Bayes.- Chapter. 9. Support Vector Machines: A supervised learning algorithm for Classification and Regression.- Chapter. 10. Clustering Overview.- Chapter. 11. Centroid-based Clustering.- Chapter. 12. Connectivity-based Clustering.- Chapter. 13. Gaussian Mixture Model.- Chapter. 14. Density-based.- Chapter. 15.- BIRCH.- Chapter. 16. CLARANS.- Chapter. 17. Affinity Propagation Clustering.- Chapter. 18. STING.- Chapter. 19. CLIQUE.- Chapter. 20. Artificial Neural Networks.- Chapter. 21. ANN-based Applications.- Chapter. 22. Automated Tools.- Chapter. 23. DataScientist’s Ultimate Workflow.