Python for Programmers with Introductory AI Case Studies

December 10, 2020
Python for Programmers with Introductory AI Case Studies

The professional programmer’s Deitel® guide to Python®with introductory artificial intelligence case studiesWritten for programmers with a background in another high-levellanguage, Python for Programmers useshands-on instruction to teach today’s most compelling, leading-edgecomputing technologies and programming in Python–one of the world’smost popular and fastest-growing languages. Please read the Tableof Contents diagram inside the front cover and the Preface for moredetails.In the context of 500+, real-world examples ranging fromindividual snippets to 40 large scripts and full implementationcase studies, you’ll use the interactive IPython interpreter withcode in Jupyter Notebooks to quickly master the latest Pythoncoding idioms. After covering Python Chapters 1-5 and a few keyparts of Chapters 6-7, you’ll be able to handle significantportions of the hands-on introductory AI case studies in Chapters11-16, which are loaded with cool, powerful, contemporary examples.These include natural language processing, data mining Twitter® forsentiment analysis, cognitive computing with IBM® Watson™,supervised machine learning with classification and regression,unsupervised machine learning with clustering, computer visionthrough deep learning and convolutional neural networks, deeplearning with recurrent neural networks, big data with Hadoop®,Spark™ and NoSQL databases, the Internet of Things and more. You’llalso 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 tocase studies• IPython + code in Jupyter Notebooks• Library-focused: Uses Python Standard Library and data sciencelibraries 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, datamining 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. Register your product for convenientaccess to downloads, updates, and/or corrections as they becomeavailable.