Python for Programmers: with Big Data and Artificial Intelligence Case Studies,Used

The professional programmers Deitel guide to Pythonwith introductory artificial intelligence case studiesWritten for programmers with a background in another highlevel language, this book uses handson instruction to teach todays most compelling, leadingedge computing technologies and programming in Pythonone of the worlds most popular and fastestgrowing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.In the context of 500+, realworld examples ranging from individual snippets to 40 large scripts and full implementation case studies, youll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 15 and a few key parts of Chapters 67, youll be able to handle significant portions of the handson introductory AI case studies in Chapters 1116, 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. Youll also work directly or indirectly with cloudbased services, including Twitter, Google Translate, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.Features 500+ handson, realworld, livecode examples from snippets to case studies IPython + code in Jupyter Notebooks Libraryfocused: 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, functionalstyle and objectoriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with realworld 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 Opensource libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikitlearn, Keras and more. Register your product for convenient access to downloads, updates, and/or corrections as they become available.