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Data Scientist Pocket Guide Over 600 Concepts, Terminologies, and Processes of Machine Learning and Deep Learning Assembled Together ['Sabri, Mohamed']

Par : Contributeur(s) : Type de matériel : TexteTexteÉditeur : BPB Publications 2021Description : pType de contenu :
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  • 9789390684977
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Ressources en ligne : Abrégé : Discover one of the most complete dictionaries in data science.Key Features? Simplified understanding of complex concepts, terms, terminologies, and techniques.? Combined glossary of machine learning, mathematics, and statistics.? Chronologically arranged A-Z keywords with brief description.DescriptionThis pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning.What you will learn? Get absolute clarity on every concept, process, and algorithm used in the process of data science operations.? Keep yourself technically strong and sound-minded during data science meetings.? Strengthen your knowledge in the field of Big data and business intelligence.Who this book is forThis book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science.Table of Contents1. Chapter one: A2. Chapter two: B3. Chapter three: C4. Chapter four: D5. Chapter five: E6. Chapter six: F7. Chapter seven: G8. Chapter eight: H9. Chapter nine: I10. Chapter ten: J11. Chapter 11: K12. Chapter 12: L13. Chapter 13: M14. Chapter 14: N15. Chapter 15: O16. Chapter 16: P17. Chapter 17: Q18. Chapter 18: R19. Chapter 19 : S20. Chapter 20 : T21. Chapter 21 : U22. Chapter 22 : V23. Chapter 23: W24. Chapter 24: X25. Chapter 25: Y26. Chapter 26 : ZAbout the Authors Mohamed Sabri is the Director of Practice in Data Science and Artificial Intelligence in a business consulting firm. Thanks to his experience in the IT world, he is able to deliver end-to-end solutions in the field of AI. He is very strong in communication and well versed in technology popularization for complex projects. He has participated as a data scientist in several AI projects for large organizations such as banks and manufacturers. He has graduated in Economics and Mathematics from the University of Ottawa.Blog links: https://www.datalyticsbusiness.ca/LinkedIn Profile: https://www.linkedin.com/in/mohamed-sabri/
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Discover one of the most complete dictionaries in data science.Key Features? Simplified understanding of complex concepts, terms, terminologies, and techniques.? Combined glossary of machine learning, mathematics, and statistics.? Chronologically arranged A-Z keywords with brief description.DescriptionThis pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning.What you will learn? Get absolute clarity on every concept, process, and algorithm used in the process of data science operations.? Keep yourself technically strong and sound-minded during data science meetings.? Strengthen your knowledge in the field of Big data and business intelligence.Who this book is forThis book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science.Table of Contents1. Chapter one: A2. Chapter two: B3. Chapter three: C4. Chapter four: D5. Chapter five: E6. Chapter six: F7. Chapter seven: G8. Chapter eight: H9. Chapter nine: I10. Chapter ten: J11. Chapter 11: K12. Chapter 12: L13. Chapter 13: M14. Chapter 14: N15. Chapter 15: O16. Chapter 16: P17. Chapter 17: Q18. Chapter 18: R19. Chapter 19 : S20. Chapter 20 : T21. Chapter 21 : U22. Chapter 22 : V23. Chapter 23: W24. Chapter 24: X25. Chapter 25: Y26. Chapter 26 : ZAbout the Authors Mohamed Sabri is the Director of Practice in Data Science and Artificial Intelligence in a business consulting firm. Thanks to his experience in the IT world, he is able to deliver end-to-end solutions in the field of AI. He is very strong in communication and well versed in technology popularization for complex projects. He has participated as a data scientist in several AI projects for large organizations such as banks and manufacturers. He has graduated in Economics and Mathematics from the University of Ottawa.Blog links: https://www.datalyticsbusiness.ca/LinkedIn Profile: https://www.linkedin.com/in/mohamed-sabri/

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