15 Math Concepts Every Data Scientist Should Know : Understand and learn how to apply the math behind data science algorithms
Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithms. Key Features: Understand key data science algorithms with Python-based examples ; Increase the impact of your data science solutions by lea...
Guardat en:
| Autor principal: | Hoyle, David |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Birmingham :
Packt Publishing
2024.
Paris : Cyberlibris |
| Accés en línia: | Accès Université d'Orléans et IFPM |
| Nota: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781837631940.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • 15 Math Concepts Every Data Scientist Should Know, Understand and learn how to apply the math behind data science algorithms, David Hoyle, Birmingham, Packt Publishing, 2024, 1 vol. (510 p.), 978-18-3763-418-7 |
Ítems similars
-
40 Algorithms Every Data Scientist Should Know : Navigating through essential AI and ML algorithms
(Livre numérique)
Weichenberger, Jürgen, et al.
BPB Publications, 2024 -
Bash for Data Scientists
(Livre numérique)
Campesato, Oswald
Mercury Learning and Information, 2022 -
Data scientist et mlops
(Thèse et Mémoire papier)
Pulgarin Gonzalez, Juan Pablo
[s.n.], 2020 -
What Should I Know About my Teen?
(Livre numérique)
Boisvert, Céline
Editions du CHU Sainte-Justine, 2008 -
Brain Oscillations and Predictive Coding: What We Know and What We Should Learn
(Livre numérique)
Roumen Kirov
Frontiers Media SA, 2021

