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The professional programmer’s Deitel guide to Pythonwith introductory artificial intelligence case studies

Written for programmers with a background in another high-level language, this book 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 Watsonâ„¢, machine learning, deep learning, computer vision, Hadoop, Sparkâ„¢, 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.

7 reviews for Python for Programmers: with Big Data and Artificial Intelligence Case Studies

  1. Python for Programmers: with Big Data and Artificial Intelligence Case Studies photo review
    Antonio
    April 15, 2021
    Ottimo
    Libro perfetto ottima fattura e contiene tantissimi concetti per programmare in python.
    Helpful? 0 0
    Hugo Marquez
    March 8, 2021
    Excelente producto y tiempo de entrega
    Excelente producto y tiempo de entrega
    Helpful? 0 0
    Cristian Scutaru
    February 18, 2020
    Huge Bible, a bit messy, but with interesting information
    This is a huge book with a cocktail of information on Python and almost anything else related to Machine Learning and Natural Language Processing.A bi...More
    This is a huge book with a cocktail of information on Python and almost anything else related to Machine Learning and Natural Language Processing.

    A bit hard to follow, but interesting and useful if chapters read separately.

    **Later Edit** I'll switch to 5-stars because - compared to other books I've read lately - this one is close to exceptional with most of the information it contains. The first half is a manual on the Python language, and IMHO this should have been in a separate book.

    The second part covers, in separate chapters, important areas from AI:
    o Natural Language Processing - with TextBlot…
    o Data Mining - with Twitter
    o Cognitive Computing - with IBM Watson
    o Machine Learning - with Classification, Regression and Clustering
    o Deep Learning - with Keras, TensorFlow etc
    o Big Data - with Hadoop, Spark, NoSQL and IoT

    These last chapters alone are rather short, but pure gold. If you don't have time to read the equivalent of several books on AI, each covering one of these topics, these quick references are good enough to give you a good idea where we are.
    Helpful? 0 0
    Tony
    September 25, 2019
    Excellent book
    I love the Deitel books. Their Java book is or was the best and this Python tome is the same. This book is for real programmers.
    Helpful? 0 0
    Abel N F Neto
    August 23, 2019
    Excelente livro, com conteúdo apresentado de modo didático e pratico
    Helpful? 0 0
    d
    June 26, 2019
    The book is well written and informative. Concrete examples are provided to leverage and test your knowledge of the subject matter. I received the bo...More
    The book is well written and informative. Concrete examples are provided to leverage and test your knowledge of the subject matter. I received the book for free to review and I ended up buying a copy for myself as a gift to my nephew majoring in engineering. The topics are discussed in a user friendly and relate able way.
    Helpful? 0 0
    Alessandro A Rizzo
    May 12, 2019
    Excellent book, recommended
    Coming from a Java background and trying to get into ML, this book is excellent in readability and content. It goes through the relevant Python struct...More
    Coming from a Java background and trying to get into ML, this book is excellent in readability and content. It goes through the relevant Python structure, syntax, and libraries, but it assumes knowledge of object-oriented programming so it introduces these topics quickly. This is not a book for someone with no experience. It took me about 2 weeks to work through the entire book (outside my job), including typing in all code (muscle memory!). In the end, I feel confident enough to read Python code and write Python scripts to tie together library functions. With respect to data science and big data, each chapter (1-10) has a small section that is devoted to the topic and the final 6 chapters (11-16) have worked examples using scikit-learn, Keras/TensorFlow, and others. I can hardly imagine how this book could be improved upon. Recommended!
    Helpful? 0 0
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