Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reducti
(eBook)

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Published
May Reads, 2024.
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Format
eBook
Language
English
ISBN
9798224213085

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APA Citation, 7th Edition (style guide)

Tom Lesley., & Tom Lesley|AUTHOR. (2024). Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reducti . May Reads.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Tom Lesley and Tom Lesley|AUTHOR. 2024. Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reducti. May Reads.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Tom Lesley and Tom Lesley|AUTHOR. Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reducti May Reads, 2024.

MLA Citation, 9th Edition (style guide)

Tom Lesley, and Tom Lesley|AUTHOR. Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reducti May Reads, 2024.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID35175c69-5878-28d8-8e32-fe88f9e4bc44-eng
Full titlepython machine learning for beginners unsupervised learning clustering and dimensionality reducti
Authorlesley tom
Grouping Categorybook
Last Update2024-05-14 23:01:28PM
Last Indexed2024-05-15 23:50:25PM

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    [synopsis] => Python Machine Learning for Beginners: Your Journey to Becoming a Python Machine Learning Expert is a comprehensive guide designed for individuals with little or no prior experience in machine learning. This book is designed to provide a step-by-step introduction to the world of machine learning and help you understand the concepts, tools, and techniques required to become a proficient machine learning practitioner. The book covers the fundamentals of machine learning, including supervised and unsupervised learning, feature engineering, model selection, and evaluation. You will learn how to use Python libraries such as scikit-learn, TensorFlow, and PyTorch to build and evaluate machine learning models.With a focus on practical examples and hands-on exercises, the book will help you build a solid foundation in machine learning and give you the confidence to tackle real-world projects. The book also includes a variety of case studies and projects that will help you apply the concepts you have learned to real-world situations. Whether you're a beginner or an experienced programmer, this book is the perfect resource for anyone looking to expand their skill set and become a machine learning expert. With its clear explanations, step-by-step instructions, and hands-on exercises, this book will help you get up and running with Python machine learning in no time.
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