Thinking data science : a data science practitioner's guide

By:

Sarang, Poornachandra

Series: Springer series in applied machine learningPublication details: Springer 2023 ChamDescription: 358pISBN:

9783031023620

Subject(s):

DDC classification:

006.31 SAR

Summary: The book aims to help practitioners, academicians, researchers, and students understand the various ML algorithms and their selection processes. It also discusses the consolidation of available algorithms and techniques for designing efficient ML models. The book is designed to help practitioners, academicians, researchers, and students build ML models using appropriate algorithms and architectures, regardless of the size of the data.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Jammu General Stacks	Non-fiction	006.31 SAR (Browse shelf(Opens below))	Available		IIMJ-8442

Total holds: 0

1. Data Science Process 2. Dimensionality Reduction 3. Regression Analysis 4. Decision Tree 5. Ensemble: Bagging and Boosting 6. K-Nearest Neighbors 7. Naive Bayes 8. Support Vector Machines 9. Centroid-Based Clustering 10. Connectivity-Based Clustering 11. Gaussian Mixture Model 12. Density-Based Clustering 13. BIRCH 14. CLARANS 15. Affinity Propagation Clustering 16. STING & CLIQUE 17. Artificial Neural Networks 18. ANN-Based Applications 19. Automated Tools 20. Data Scientist's Ultimate Workflow

The book aims to help practitioners, academicians, researchers, and students understand the various ML algorithms and their selection processes. It also discusses the consolidation of available algorithms and techniques for designing efficient ML models. The book is designed to help practitioners, academicians, researchers, and students build ML models using appropriate algorithms and architectures, regardless of the size of the data.

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