Python for Programmers: with Big Data and Artificial Intelligence Case Studies
Publisher : Pearson (March 22, 2019)
Language : English
Paperback : 640 pages
The professional programmer’s Deitel guide to Pythonwith introductory artificial intelligence case studies
Written for programmers with a background in another high-level language, this book 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 for sentiment analysis, cognitive computing with IBM Watson™, 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, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.
Features
500+ hands-on, real-world, live-code examples from snippets to case studies
IPython + code in Jupyter Notebooks
Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code
Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions
Procedural, functional-style and object-oriented programming
Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames
Static, dynamic and interactive visualizations
Data experiences with real-world datasets and data sources
Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression
AI, big data and cloud data science case studies: NLP, data mining Twitter, IBM Watson™, machine learning, deep learning, computer vision, Hadoop, Spark™, NoSQL, IoT
Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn, Keras and more.
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