Python for Programmers with introductory Al case studies

[LeeAndro]

English | 2019 | ISBN: 9780135231364 | 640 Pages | PDF,EPUB,MOBI | 183 MB

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 rag 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 for sennt 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. You'll also 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 to case studies

IPython + code in Jupyter 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, 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

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