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Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications (Advanced Data Analytics)
Published by CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1548307750 ISBN 13: 9781548307752
Language: English
Condition: Very Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Markov Models Supervised and Unsupervised Machine Learning : Mastering Data Science & Python
Published by CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1976050006 ISBN 13: 9781976050008
Language: English
US$ 15.37
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Machine Learning Algorithms & Markov Models Supervised And Unsupervised Learning with Python & Data Science 2 Manuscripts in 1 Book:
Published by CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978170955 ISBN 13: 9781978170957
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 24.70
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Machine Learning and Data Analytics for Solving Business Problems: Methods, Applications, and Case Studies (Unsupervised and Semi-Supervised Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 194.00
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Machine Learning and Data Analytics for Solving Business Problems: Methods, Applications, and Case Studies (Unsupervised and Semi-Supervised Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 194.00
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Advancing Pandemic Prediction With Big Data and Machine Learning : Early Pandemic Prediction with Big Data: Unified Approach Using Supervised & Unsupervised Learning. DE
Published by Omniscriptum, LAP Lambert Academic Publishing, 2025
ISBN 10: 6208420555 ISBN 13: 9786208420550
Language: English
US$ 94.69
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book has presented the development of a prediction technique in big data analytics, incorporating both unsupervised and supervised learning perspectives. It has addressed the challenges of handling large volumes of complex data and provided insights into the prediction process. The research findings highlight the performance and applicability of the developed technique in various domains, showcasing its potential for practical implementation. The book contributes to the field of big data analytics by advancing the understanding of prediction techniques and providing recommendations for further research to enhance their capabilities. It has showcased the potential of these techniques to extract meaningful patterns, make accurate predictions, and generate valuable insights from vast and diverse datasets. The research outcomes open up new opportunities for organisations to harness the power of big data and make data-driven decisions that can drive innovation, efficiency, and success.
