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Python for Programmers With Introductory AI 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® 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. 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
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® 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. 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
601 pages, Paperback
Published March 22, 2019
35 people are currently reading
68 people want to read
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Displaying 1 - 5 of 5 reviews
January 31, 2024
Python, Just For Me
As a programmer, this book is a great reference.
Although I am a daily user of Python, and know it inside-and-out, this book helped with some really interesting learning and solidifying terms and knowledge for me.
I also enjoyed the book's chapters on artificial intelligence.
Definitely check out this book if you are a programmer and want to be better at Python.
4.8/5
As a programmer, this book is a great reference.
Although I am a daily user of Python, and know it inside-and-out, this book helped with some really interesting learning and solidifying terms and knowledge for me.
I also enjoyed the book's chapters on artificial intelligence.
Definitely check out this book if you are a programmer and want to be better at Python.
4.8/5
December 29, 2019
Good book. One word of caution, you really ought to have some experience with another language before reading this.
If you’re coming from another language and have some experience, this will catch you up on some of the particulars of Python.
If you’re coming from another language and have some experience, this will catch you up on some of the particulars of Python.
February 13, 2020
Good introduction to Python. This will be mostly a light refresher if you're already familiar with Python or intro stats. And, since this is a survey, the 'big data' and AI case studies are basic descriptions of some tools and probably won't be of practical use to most readers.
October 11, 2020
Not impressing — one hundred pages of introduction, five hundred pages of Python language details and short reviews of existing technologies (many of them) in the rest two hundred pages.
June 4, 2025
Well explained with a big bunch of examples but meanwhile a bit outdated.
Nonetheless, it helps to understand the basics principles.
Nonetheless, it helps to understand the basics principles.
Displaying 1 - 5 of 5 reviews