Machine Learning and Data Science : Fundamentals and Applications /
Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and...
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| Other Authors: | , , , , |
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| Format: | Book |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
2022
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| Series: | Advances in data engineering and machine learning
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| Subjects: | |
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| 050 | 0 | 0 | |a QA 76.9.D343 |
| 090 | |a QA 76.9.D343 |b M334 2022 | ||
| 245 | 0 | 0 | |a Machine Learning and Data Science : |b Fundamentals and Applications / |c Edited by Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan and Nisheeth Joshi |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c 2022 | |
| 264 | 4 | |c ©2022 | |
| 300 | |a xvi, 247 pages : |b illustrations ; |c 24 cm | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a unmediated |2 rdamedia | ||
| 338 | |a volume |2 rdacarrier | ||
| 490 | 1 | |a Advances in data engineering and machine learning | |
| 504 | |a Includes bibliographical references and index | ||
| 520 | |a Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation | ||
| 650 | 0 | |a Data mining | |
| 650 | 0 | |a Machine learning | |
| 700 | 1 | |a Agrawal, Prateek, |e editor | |
| 700 | 1 | |a Gupta, Charu, |e editor | |
| 700 | 1 | |a Sharma, Anand, |e editor | |
| 700 | 1 | |a Madaan, Vishu, |e editor | |
| 700 | 1 | |a Joshi, Nisheeth, |e editor | |
| 830 | 0 | |a Advances in data engineering and machine learning | |
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