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 <\/p>\n

\"\u062f\u0627\u0646\u0644\u0648\u062f<\/div>\n\n\n\n
\n

\u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc:<\/h3>\n<\/td>\n

\n

Python for Programmers: with Big Data and Artificial Intelligence Case Studies<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\u0633\u0627\u0644 \u0627\u0646\u062a\u0634\u0627\u0631: 2019\u00a0\u00a0|\u00a0\u00a0810 \u0635\u0641\u062d\u0647\u00a0\u00a0|\u00a0\u00a0\u062d\u062c\u0645 \u0641\u0627\u06cc\u0644: 27 \u0645\u06af\u0627\u0628\u0627\u06cc\u062a\u00a0\u00a0|\u00a0\u00a0\u0632\u0628\u0627\u0646: \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u0647<\/td>\nPaul J. Deitel, Harvey Deitel<\/td>\n<\/tr>\n
\u0646\u0627\u0634\u0631<\/td>\nPearson Higher Ed<\/td>\n<\/tr>\n
ISBN10:<\/td>\n0135224330<\/td>\n<\/tr>\n
ISBN13:<\/td>\n9780135224335<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628<\/h2>\n
\n

The professional programmer\u2019s Deitel guide to Pythonwith introductory artificial intelligence case studies<\/p>\n

Written for programmers with a background in another high-level language, this book uses hands-on instruction to teach today\u2019s most compelling, leading-edge computing technologies and programming in Python\u2013one of the world\u2019s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.<\/p>\n

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you\u2019ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1\u20145 and a few key parts of Chapters 6\u20147, you\u2019ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11\u201416, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter for sentiment analysis, cognitive computing with IBM Watson\u2122, 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\u2122 and NoSQL databases, the Internet of Things and more. You\u2019ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate\u2122, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.<\/p>\n

Features<\/p>\n

500+ hands-on, real-world, live-code examples from snippets to case studies
\nIPython + code in Jupyter Notebooks
\nLibrary-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code
\nRich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions
\nProcedural, functional-style and object-oriented programming
\nCollections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames
\nStatic, dynamic and interactive visualizations
\nData experiences with real-world datasets and data sources
\nIntro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression
\nAI, big data and cloud data science case studies: NLP, data mining Twitter, IBM Watson\u2122, machine learning, deep learning, computer vision, Hadoop, Spark\u2122, NoSQL, IoT
\nOpen-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn, Keras and more.<\/p>\n<\/div>\n

\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u0631\u0627\u0647\u0646\u0645\u0627\u06cc Deitel \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633 \u062d\u0631\u0641\u0647 \u0627\u06cc \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0645\u0648\u0631\u062f\u06cc \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc PythonWith<\/p>\n

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\u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 500+ \u060c \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0627\u0632 \u0642\u0637\u0639\u0647 \u0647\u0627\u06cc \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u062a\u0627 40 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0632\u0631\u06af \u0648 \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0645\u0648\u0631\u062f\u06cc \u0627\u062c\u0631\u0627\u06cc \u06a9\u0627\u0645\u0644 \u060c \u0634\u0645\u0627 \u0627\u0632 \u0645\u062a\u0631\u062c\u0645 \u062a\u0639\u0627\u0645\u0644\u06cc IPython \u0628\u0627 \u06a9\u062f \u062f\u0631 \u0646\u0648\u062a \u0628\u0648\u06a9 \u0647\u0627\u06cc Jupyter \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f \u062a\u0627 \u0628\u0647 \u0633\u0631\u0639\u062a \u0628\u0647 \u0633\u0631\u0639\u062a \u0622\u062e\u0631\u06cc\u0646 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0627 \u062a\u0633\u0644\u0637 \u062f\u0647\u06cc\u062f.\u067e\u0633 \u0627\u0632 \u067e\u0648\u0634\u0627\u0646\u062f\u0646 \u0641\u0635\u0644 \u0647\u0627\u06cc Python 1-5 \u0648 \u0686\u0646\u062f \u0628\u062e\u0634 \u0627\u0635\u0644\u06cc \u0627\u0632 \u0641\u0635\u0644 \u0647\u0627\u06cc 6-7 \u060c \u0634\u0645\u0627 \u0642\u0627\u062f\u0631 \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0648\u062f \u0628\u062e\u0634 \u0647\u0627\u06cc \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0627\u0632 \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0645\u0648\u0631\u062f\u06cc \u0645\u0642\u062f\u0645\u0627\u062a\u06cc AI \u0631\u0627 \u062f\u0631 \u0641\u0635\u0644 \u0647\u0627\u06cc 11-16 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f \u060c \u06a9\u0647 \u062f\u0627\u0631\u0627\u06cc \u062e\u0646\u06a9 \u0648 \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0647\u0633\u062a\u0646\u062f.\u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u0645\u0639\u0627\u0635\u0631.\u0627\u06cc\u0646 \u0645\u0648\u0627\u0631\u062f \u0634\u0627\u0645\u0644 \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u060c \u062f\u0627\u062f\u0647 \u06a9\u0627\u0648\u06cc \u062a\u0648\u06cc\u06cc\u062a\u0631 \u0628\u0631\u0627\u06cc \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u060c \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0634\u0646\u0627\u062e\u062a\u06cc \u0628\u0627 IBM Watson \u2122 \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u062d\u062a \u0646\u0638\u0627\u0631\u062a \u0628\u0627 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0648 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062f\u0633\u062a\u06af\u0627\u0647 \u0628\u062f\u0648\u0646 \u0646\u0638\u0627\u0631\u062a \u0628\u0627 \u062e\u0648\u0634\u0647 \u0628\u0646\u062f\u06cc \u060c \u062f\u06cc\u062f \u0631\u0627\u06cc\u0627\u0646\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0648 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc Convolutional \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0645\u06a9\u0631\u0631 \u0627\u0633\u062a.\u060c \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0628\u0627 \u067e\u0627\u06cc\u06af\u0627\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc Hadoop \u060c Spark \u2122 \u0648 NoSQL \u060c \u0627\u06cc\u0646\u062a\u0631\u0646\u062a \u0627\u0634\u06cc\u0627\u0621 \u0648 \u0645\u0648\u0627\u0631\u062f \u062f\u06cc\u06af\u0631.\u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0647 \u0637\u0648\u0631 \u0645\u0633\u062a\u0642\u06cc\u0645 \u06cc\u0627 \u063a\u06cc\u0631\u0645\u0633\u062a\u0642\u06cc\u0645 \u0628\u0627 \u062e\u062f\u0645\u0627\u062a \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0627\u0628\u0631 \u060c \u0627\u0632 \u062c\u0645\u0644\u0647 \u062a\u0648\u06cc\u06cc\u062a\u0631 \u060c Google Translate \u2122 \u060c IBM Watson \u060c Microsoft Azure \u060c OpenMapquest \u060c PubNub \u0648 \u0645\u0648\u0627\u0631\u062f \u062f\u06cc\u06af\u0631 \u06a9\u0627\u0631 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f.<\/p>\n

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\n\u0645\u0642\u062f\u0645\u0647 \u0628\u062e\u0634 \u0647\u0627\u06cc \u0639\u0644\u0648\u0645 \u062f\u0627\u062f\u0647: \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u060c \u0622\u0645\u0627\u0631 \u0627\u0633\u0627\u0633\u06cc \u060c \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc \u060c \u0627\u0646\u06cc\u0645\u06cc\u0634\u0646 \u060c \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u060c \u062f\u0631\u06af\u06cc\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u060c \u0631\u06af\u0631\u0633\u06cc\u0648\u0646
\nAI \u060c Big Data and Cloud Data Science Case Case: NLP \u060c Data Mining Twitter \u060c IBM Watson \u2122 \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u060c \u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u0631\u0627\u06cc\u0627\u0646\u0647 \u060c Hadoop \u060c Spark \u2122 \u060c NoSQL \u060c IoT
\n\u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0646\u0628\u0639 \u0628\u0627\u0632: Numpy \u060c Pandas \u060c Matplotlib \u060c Seaborn \u060c Folium \u060c Scipy \u060c NLTK \u060c TextBlob \u060c Spacy \u060c Textatistic \u060c Tweepy \u060c Scikit-Learn \u060c Keras \u0648 \u0645\u0648\u0627\u0631\u062f \u062f\u06cc\u06af\u0631.
\n
\r\n\r\n\t\r\n\t\t\r\n\t\t\r\n\t\t\r\n\t\t\r\n\t\t\r\n\t\t
\r\n\t\t\t\r\n\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u0645\u062d\u0635\u0648\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc \u0627\u0633\u062a \u0648 \u0646\u0647 \u06a9\u062a\u0627\u0628 \u06a9\u0627\u063a\u0630\u06cc.\r\n\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\r\n\t\t
\r\n\t\t\t\r\n\u0628\u0647 \u0647\u0646\u06af\u0627\u0645 \u062e\u0631\u06cc\u062f \u0628\u0647 \u0632\u0628\u0627\u0646 \u062f\u0631\u062c \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u06a9\u062a\u0627\u0628 \u062d\u062a\u0645\u0627 \u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f. \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u0639\u0645\u0648\u0644 \u062f\u0631 \u0627\u06a9\u062b\u0631 \u0645\u0648\u0627\u0631\u062f \u0632\u0628\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0641\u0627\u0631\u0633\u06cc \u0646\u06cc\u0633\u062a.\r\n\r\n\t\t<\/td>\r\n\t<\/tr>\t\r\n\r\n\t\t\t
\r\n\t\t\t\r\n\u062f\u0631 \u0635\u0648\u0631\u062a \u0647\u0631\u06af\u0648\u0646\u0647 \u0645\u0634\u06a9\u0644 \u062f\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0634\u0645\u0627\u0631\u0647 09395106248 \u067e\u06cc\u0627\u0645\u06a9 \u062f\u0647\u06cc\u062f. \r\n\t\t<\/td>\r\n\t<\/tr>\t\r\n\t\r\n\t\t\t
\r\n\t\t\t\r\n\u062f\u0631\u062c \u0634\u0645\u0627\u0631\u0647 \u0645\u0648\u0628\u0627\u06cc\u0644 \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u0636\u0631\u0648\u0631\u06cc \u0646\u06cc\u0633\u062a \u0648\u0644\u06cc \u062a\u0631\u062c\u06cc\u062d \u0622\u0646 \u0627\u0633\u062a \u062f\u0631\u062c \u06af\u0631\u062f\u062f \u062a\u0627 \u062f\u0631 \u0635\u0648\u0631\u062a \u0628\u0631\u0648\u0632 \u0645\u0634\u06a9\u0644 \u0627\u0648\u0644\u06cc\u0646 \u0631\u0627\u0647 \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u0645\u0627 \u0628\u0627 \u0634\u0645\u0627 \u0628\u0627\u0634\u062f.\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\t\r\n\t
\r\n\t\t\t\r\n\t\t\t\u0686\u0646\u0627\u0646\u0686\u0647 \u062f\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u062d\u0635\u0648\u0644 \u0628\u0647 \u0647\u0631 \u062f\u0644\u06cc\u0644\u06cc \u0628\u0627 \u0645\u0634\u06a9\u0644 \u0631\u0648\u0628\u0631\u0648 \u0634\u062f\u06cc\u062f \u0648 \u0645\u0637\u0645\u0626\u0646 \u0627\u0632 \u067e\u0631\u062f\u0627\u062e\u062a \u0645\u0648\u0641\u0642 \u0648\u062c\u0647 \u0647\u0633\u062a\u06cc\u062f \u0628\u0647 \u0634\u0645\u0627\u0631\u0647 \u062a\u0645\u0627\u0633 \u0632\u06cc\u0631 \u0646\u0627\u0645\u060c \u0646\u0627\u0645 \u062e\u0627\u0646\u0648\u0627\u062f\u06af\u06cc \u0648 \u0646\u0627\u0645 \u0645\u062d\u0635\u0648\u0644 \u0631\u0627 \u067e\u06cc\u0627\u0645\u06a9 \u0628\u0632\u0646\u06cc\u062f \u062a\u0627 \u0644\u06cc\u0646\u06a9 \u0645\u062d\u0635\u0648\u0644 \u0633\u0631\u06cc\u0639\u0627 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u0627\u0631\u0633\u0627\u0644 \u06af\u0631\u062f\u062f.\r\n\t\t\t

\r\n\t\t\t\u0634\u0645\u0627\u0631\u0647 \u062a\u0645\u0627\u0633: 09395106248 \r\n\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\t\r\n<\/table>\r\n
\r\n\r\n<\/p>\n","protected":false},"excerpt":{"rendered":"

  \u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc: Python for Programmers: with Big Data and Artificial Intelligence Case Studies \u0633\u0627\u0644 \u0627\u0646\u062a\u0634\u0627\u0631: 2019\u00a0\u00a0|\u00a0\u00a0810 \u0635\u0641\u062d\u0647\u00a0\u00a0|\u00a0\u00a0\u062d\u062c\u0645 […]<\/p>\n","protected":false},"featured_media":33998,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}}},"product_cat":[196,251,291],"product_tag":[],"class_list":{"0":"post-33997","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-196","7":"product_cat-251","8":"product_cat-291","9":"pmpro-has-access","10":"desktop-align-left","11":"tablet-align-left","12":"mobile-align-left","14":"first","15":"instock","16":"shipping-taxable","17":"purchasable","18":"product-type-variable"},"yoast_head":"\n\u06a9\u062a\u0627\u0628 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u0627\u0646\u061b \u0628\u0627 \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0645\u0648\u0631\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0648 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc - 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