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The professional programmers Deitel guide to Python with introductory artificial intelligence case studies
Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach todays most compelling, leading-edge computing technologies and programming in Pythonone of the worlds 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, youll 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, youll 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. Youll also work directly or indirectly with cloud-based services, including Twitter, Google Translate, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.
Features
Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.
Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach todays most compelling, leading-edge computing technologies and programming in Pythonone of the worlds 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, youll 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, youll 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. Youll 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
Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.
- Format: Pocket/Paperback
- ISBN: 9780135224335
- Språk: Engelska
- Antal sidor: 640
- Utgivningsdatum: 2018-10-22
- Förlag: Pearson