Thinking Data Science

A Data Science Practitioner’s Guide

Häftad, Engelska, 2024

Av Poornachandra Sarang

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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

Utgivningsdatum2024-03-02
Mått155 x 235 x 21 mm
Vikt575 g
FormatHäftad
SpråkEngelska
SerieSpringer Series in Applied Machine Learning
Antal sidor358
FörlagSpringer International Publishing AG
ISBN9783031023651

Tillhör följande kategorier

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

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.

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.