Machine Learning for Engineers: Using data to solve problems for physical systems

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(as of Dec 26,2024 23:57:15 UTC – Details)

ASIN ‏ : ‎ B09GV1CQG1
Publisher ‏ : ‎ Springer (September 21, 2021)
Publication date ‏ : ‎ September 21, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 35912 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 397 pages

Machine Learning for Engineers: Using data to solve problems for physical systems

In today’s fast-paced world, engineers are constantly faced with complex problems that require innovative solutions. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for engineers to analyze data and make informed decisions.

Machine learning algorithms can be trained on large datasets to recognize patterns and make predictions, allowing engineers to optimize processes, improve performance, and solve problems in physical systems more efficiently.

By harnessing the power of machine learning, engineers can uncover valuable insights from data that may not be immediately apparent through traditional analysis methods. Whether it’s predicting equipment failures, optimizing energy usage, or improving product designs, machine learning offers engineers a versatile and powerful tool to tackle a wide range of challenges.

In this post, we’ll explore how engineers can leverage machine learning to enhance their problem-solving capabilities and drive innovation in the field of engineering. Stay tuned for more insights on how data-driven approaches can revolutionize the way engineers approach complex problems in physical systems.
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