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 500+ hands-on,
real-world, live-code examples from snippets to case studies
IPython + code in Jupyter (R) 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 (R), IBM (R) Watson (TM), machine learning,
deep learning, computer vision, Hadoop (R), Spark (TM), NoSQL, IoT
Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium,
SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn
(R), Keras and more Register your product for convenient access to
downloads, updates, and/or corrections as they become available.
See inside book for more information.
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