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“T-SQL Fundamentals 3rd Edition by Itzik Ben-Gan, ISBN-13: 978-1509302000” has been added to your cart. View cart
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Python 3 for Machine Learning by Oswald Campesato, ISBN-13: 978-1683924951
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Programming Logic and Design, Comprehensive by Joyce Farrell, ISBN-13: 978-1337102070
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Educational Research: Quantitative, Qualitative, and Mixed Approaches 7th Edition, ISBN-13: 978-1544337838
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Python 3 for Machine Learning by Oswald Campesato, ISBN-13: 978-1683924951

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SKU: python-3-for-machine-learning-by-oswald-campesato-isbn-13-978-1683924951 Category: Computing Tags: ISBN-10: 1683924959, ISBN-13: 978-1683924951, Oswald Campesato, Python 3 for Machine Learning by Oswald Campesato
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Description

Python 3 for Machine Learning by Oswald Campesato, ISBN-13: 978-1683924951 

[PDF eBook eTextbook]

  • Publisher: ‎ Mercury Learning and Information (March 2, 2020)
  • Language: ‎ English
  • 364 pages
  • ISBN-10: ‎ 1683924959
  • ISBN-13: ‎ 978-1683924951

This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2.

Features:

  • Provides the reader with basic Python 3 programming concepts related to machine learning
  • Includes separate appendices for regular expressions, Keras, and TensorFlow 2

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, TensorFlow, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning and Information).

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