Python for Programmers Contributor(s): Deitel, Paul (Author), Deitel, Harvey (Author) |
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ISBN: 0135224330 ISBN-13: 9780135224335 Publisher: Pearson
WE WILL NOT BE UNDERSOLD! Click here for our low price guarantee Binding Type: Paperback Published: March 2019 Click for more in this series: Ellis Horwood Series in Applied Science and Industrial Techn |
Additional Information |
BISAC Categories: - Computers | Programming Languages - Python - Computers | Databases - Data Mining - Computers | Intelligence (ai) & Semantics |
LCCN: 69015341 |
Series: Ellis Horwood Series in Applied Science and Industrial Techn |
Physical Information: 1.2" H x 7.1" W x 9.1" L (2.10 lbs) 640 pages |
Themes: - Chronological Period - 20th Century |
Features: Bibliography, Illustrated, Index, Price on Product |
Descriptions, Reviews, Etc. |
Publisher Description: The professional programmer's Deitel(R) guide to Python(R) with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today's most compelling, leading-edge computing technologies and programming in Python--one of the world's most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you'll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you'll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter(R) for sentiment analysis, cognitive computing with IBM(R) Watson(TM), supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop(R), Spark(TM) and NoSQL databases, the Internet of Things and more. You'll also work directly or indirectly with cloud-based services, including Twitter, Google Translate(TM), IBM Watson, Microsoft(R) Azure(R), OpenMapQuest, PubNub and more. Features
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