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Deitel Developer Series: Python for Programmers With Introductory AI Case Studies

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Designed for programmers transitioning from another high-level language, this resource employs hands-on instruction to teach Python, one of the fastest-growing programming languages. It features over 500 real-world examples, including snippets and extensive scripts, utilizing the interactive IPython interpreter and Jupyter Notebooks to help you quickly grasp modern Python coding practices. After exploring the initial chapters, you'll be equipped to tackle significant portions of hands-on AI case studies filled with contemporary examples. These cover a range of topics such as natural language processing, sentiment analysis through Twitter data, cognitive computing with IBM Watson, supervised and unsupervised machine learning, computer vision via deep learning, and big data technologies like Hadoop and Spark. The book also includes cloud-based services interactions with platforms like Google Translate and Microsoft Azure. Key features include a focus on the Python Standard Library and data science libraries, rich coverage of control statements, functions, and collections, as well as static and dynamic visualizations. It also introduces data science concepts, including AI, statistics, and data wrangling, alongside practical case studies in AI, big data, and cloud data science. Open-source libraries like NumPy, pandas, and scikit-learn are extensively utilized throughout.

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Deitel Developer Series: Python for Programmers With Introductory AI Case Studies, Paul J. Deitel, Harvey M. Deitel

Language
Released
2019
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Title
Deitel Developer Series: Python for Programmers With Introductory AI Case Studies
Language
English
Released
2019
Format
Paperback
Pages
601
ISBN10
0135224330
ISBN13
9780135224335
Series
Description
Designed for programmers transitioning from another high-level language, this resource employs hands-on instruction to teach Python, one of the fastest-growing programming languages. It features over 500 real-world examples, including snippets and extensive scripts, utilizing the interactive IPython interpreter and Jupyter Notebooks to help you quickly grasp modern Python coding practices. After exploring the initial chapters, you'll be equipped to tackle significant portions of hands-on AI case studies filled with contemporary examples. These cover a range of topics such as natural language processing, sentiment analysis through Twitter data, cognitive computing with IBM Watson, supervised and unsupervised machine learning, computer vision via deep learning, and big data technologies like Hadoop and Spark. The book also includes cloud-based services interactions with platforms like Google Translate and Microsoft Azure. Key features include a focus on the Python Standard Library and data science libraries, rich coverage of control statements, functions, and collections, as well as static and dynamic visualizations. It also introduces data science concepts, including AI, statistics, and data wrangling, alongside practical case studies in AI, big data, and cloud data science. Open-source libraries like NumPy, pandas, and scikit-learn are extensively utilized throughout.