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
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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