ISBN-13: 9781032878171, 978-1032878171. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and MATLAB, which are available on GitHub and can be run from there in Binder in a web browser.
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Publisher: CRC Press; 1st edition (15 July 2024). Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns make predictions and continually enhance performance.
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ISBN-13: 9781637427484, 978-1637427484. Both supervised and unsupervised learning ML models are [url] include data collection and feature engineering techniques as well as regression, classification, neural networks (deep learning), and clustering.
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ISBN-13: 9780367698201, 978-0367698201. Knowledge Guided Machine Learning. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios.
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Publish Year: Dezember 2020. Films Music Books Textbooks Audiobooks Antiquarian books. In rare cases, a book may differ from the stated edition. Weight: 397 g. Each item is manually inspected and evaluated by us.
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Author: Samuel Hack. Machine Learning: 4 Books in 1: A Complete Overview for Beginners to Master the Basics of Python Programming and Understand How to Build. Publish Year: Mai 2021. Size: 3.8 cm x 15.7 cm x 23.5 cm.
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Subtitle A Beginner's Guide with Ethical Insights. Mathematics for Machine Learning 3. Data Preparation 4. Machine Learning Operations 5. Responsible AI and Explainable AI 7. Machine Learning Step-by-Step Practical Examples.
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ISBN-13: 9780367277321, 978-0367277321. Supervised Machine Learning. Key Features Format: Hardback. Author(s): Tanya Kolosova, Samuel Berestizhevsky.
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