Python for Programmers: with Big Data and Artificial Intelligence Case Studies – PDF

Python for Programmers: with Big Data and Artificial Intelligence Case Studies (in PDF) is the expert developer’s Deitel guide to Python with initial expert system case studies. Written for developers with a background in another high- level language, this ebook utilizes hands- on guideline to teach today’s most engaging, leading- edge computing innovations and programs in Python– among the world’s most fastest- growing and popular programs languages. Please checked out the Table of Contents diagram inside the front cover and the Preface for more information. In the context of 500+, genuine- world examples varying from specific bits to 40 big scripts and complete application case studies, you’ll utilize the interactive IPython interpreter with code in Jupyter Notebooks to rapidly master the most recent Python coding idioms. After covering Python Chapters 1– 5 and a couple of crucial parts of Chapters 6– 7, you’ll have the ability to deal with considerable parts of the hands- on initial AI case studies in Chapters 11– 16, which are filled with cool, effective, modern examples. These consist of natural language processing, data mining Twitter for belief analysis, cognitive computing with IBM Watson &#x 2122;, monitored artificial intelligence with category and regression, not being watched artificial intelligence with clustering, computer system vision through deep knowing and convolutional neural networks, deep knowing with frequent neural networks, big data with Hadoop, Spark &#x 2122; and No SQL databases, the Internet of Things and more. You’ll likewise work indirectly or straight with cloud- based services, consisting of Twitter, IBM Watson, Google Translate &#x 2122;, Open MapQuest, PubNub, Microsoft Azure, and more. Features

• IPython + code in Jupyter Notebooks
• Static, interactive and vibrant visualizations
• Data experiences with genuine- world datasets and data sources
• Procedural, practical- design and item- oriented programs
• 500+ hands- on, genuine- world, live- code examples from bits to case studies
• Collections: Lists, tuples, dictionaries, sets, NumPy ranges, pandas Series & &Data Frames
• Rich Python protection:Control declarations, functions, strings, files, JSON serialization, CSV, exceptions
• Intro to Data Science areas: AI, standard statistics, simulation, animation, random variables, data wrangling, regression
• Library- focused: Uses Python Standard Library and data science libraries to achieve considerable jobs with very little code
• Open- source libraries: NumPy, pandas, Seaborn, Matplotlib, scikit- discover, SciPy, NLTK, Folium, spaCy, Textatistic, TextBlob, Tweepy, Keras and more.
• AI, big data and cloud data science case studies: NLP, IBM Watson &#x 2122;, artificial intelligence, data mining Twitter, computer system vision, Hadoop, deep knowing, Spark &#x 2122;, IoT, No SQL and so on

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Reviews

“For a while, I have actually been looking for an ebook in Data Science utilizing Python that would cover the most appropriate innovations. Well, my search is over. A should- have book for any professional of this field. The artificial intelligence chapter is a genuine winner!! The vibrant visualization is wonderful” — Ramon Mata-Toledo, Professor, James Madison University “ I like the brand-new mix of subjects from computer technology, data science, and statistics. This is necessary for structure data science programs that are more than simply patching together mathematics and computer technology courses. A book like this might assist assist in broadening our offerings and utilizing Python as a bridge for computer system and data science subjects. For a data science program that concentrates on a single language (mainly), I believe Python is most likely the method to go” — Lance Bryant, Shippensburg University “Covers a few of the most modern-day Python syntax methods and presents neighborhood requirements for design and paperwork. The artificial intelligence chapter does a fantastic task of strolling individuals through the boilerplate code required for ML inPython The case studies achieve this actually well. The later on examples are so visual. Many of the design examination jobs make for actually excellent programs practice. I can see readers feeling actually delighted about playing with the animations” — Elizabeth Wickes, Lecturer, School of Information Sciences, the University of Illinois at Urbana-Champaign “ A terrific intro to Big Data principles, especially Hadoop, Spark, and IoT. The examples are very practical and useful. The authors do an exceptional task of integrating programs and data science subjects. The product exists in absorbable areas accompanied by engaging interactive examples. Nearly all principles are accompanied by a worked- out example. An extensive summary of item- oriented programs in Python– making use of card image graphics makes certain to engage the trainees” — Garrett Dancik, Eastern Connecticut State University “An appealing, extremely available ebook that will promote interest and encourage starting data researchers to establish necessary structures in Python programs, data, data control, working with APIs, data visualization, artificial intelligence, cloud computing, and more. Great walkthrough of the Twitter APIs– belief analysis piece is really beneficial. I have actually taken numerous classes that cover natural language processing and this is the very first time the tools and principles have actually been described so plainly. I value the conversation of serialization with JSON and pickling and when to utilize one or the other–with a focus on utilizing JSON over pickle– excellent to understand there’s a more secure, much better method!” — Jamie Whitacre, Data Science Consultant “The chapters are really plainly composed with in-depth descriptions of the example code. The modular structure, a vast array of modern data science subjects, and code in buddy Jupyter note pads make this a wonderful resource for readers of a range of backgrounds. Fabulous Big Data chapter– it covers all of the appropriate programs and platforms. Great Watson chapter! The chapter offers a fantastic summary of the Watson applications. Also, your translation examples are terrific since they offer an ‘instantaneous benefit’– it’s really pleasing to carry out a job and get outcomes so rapidly. Machine Learning is a substantial subject, and the chapter works as a fantastic intro. I enjoyed the California real estate data example– really appropriate for service analytics. The chapter was aesthetically spectacular” — Alison Sanchez, Assistant Professor in Economics, University of San Diego NOTE: Only the PDF ebook Python For Programmers is consisted of in this purchase. No other resources are consisted of