Cover art for Python for Programmers
Published
Prentice Hall Australia, November 2018
ISBN
9780135224335
Format
Hardcover, 640 pages
Dimensions
23cm × 17.8cm × 3.2cm

Python for Programmers With Big Data and Artificial Intelligence Case Studies

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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.

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.

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 serialisation, CSV, exceptions

Procedural, functional-style and object-oriented programming

Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames

Static, dynamic and interactive visualisations

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

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