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, 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(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
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