Thinking Data Science A Data Science Practitioner’s Guide /

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the clie...

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Kaituhi matua:	Sarang, Poornachandra (Author)
Kaituhi rangatōpū:	SpringerLink (Online service)
Hōputu:	Tāhiko īPukapuka
Reo:	Ingarihi
I whakaputaina:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Putanga:	1st ed. 2023.
Rangatū:	The Springer Series in Applied Machine Learning,
Ngā marau:	Machine learning. Artificial intelligence > Data processing. Artificial intelligence. Machine Learning. Data Science. Artificial Intelligence.
Urunga tuihono:	https://doi.org/10.1007/978-3-031-02363-7
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Rārangi ihirangi:

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.

Thinking Data Science A Data Science Practitioner’s Guide /

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