{"id":26895,"date":"2024-02-28T08:20:09","date_gmt":"2024-02-28T09:20:09","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%d8%b1%d9%88%da%98%d9%87-%d9%87%d8%a7%db%8c-%d8%b9%d9%84%d9%88%d9%85-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%db%8c%da%a9-%d8%b1\/"},"modified":"2024-10-13T21:11:26","modified_gmt":"2024-10-13T21:11:26","slug":"%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%d8%b1%d9%88%da%98%d9%87-%d9%87%d8%a7%db%8c-%d8%b9%d9%84%d9%88%d9%85-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%db%8c%da%a9-%d8%b1","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%d8%b1%d9%88%da%98%d9%87-%d9%87%d8%a7%db%8c-%d8%b9%d9%84%d9%88%d9%85-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%db%8c%da%a9-%d8%b1\/","title":{"rendered":"\u06a9\u062a\u0627\u0628 \u067e\u0631\u0648\u0698\u0647 \u0647\u0627\u06cc \u0639\u0644\u0648\u0645 \u062f\u0627\u062f\u0647 \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646: \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0645\u0637\u0627\u0644\u0639\u0647 \u0645\u0648\u0631\u062f\u06cc \u0628\u0631\u0627\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0628\u06cc\u0646\u0634 \u0627\u0631\u0632\u0634\u0645\u0646\u062f \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0628\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u060c \u0646\u0633\u062e\u0647 2"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div style=\"text-align: center;\"><img decoding=\"async\" title=\"\u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u062a\u0627\u0628 Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition\" src=\"https:\/\/express24.ir\/d\/wp-content\/uploads\/2024\/02\/9781800564480.jpg\" alt=\"\u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u062a\u0627\u0628 Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition\" \/><\/div>\n<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\n<h3>\u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc:<\/h3>\n<\/td>\n<td>\n<h3>Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0633\u0627\u0644 \u0627\u0646\u062a\u0634\u0627\u0631: 2021\u00a0\u00a0|\u00a0\u00a0432 \u0635\u0641\u062d\u0647\u00a0\u00a0|\u00a0\u00a0\u062d\u062c\u0645 \u0641\u0627\u06cc\u0644: 17 \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<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u0647<\/td>\n<td>Stephen Klosterman<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0627\u0634\u0631<\/td>\n<td>Packt Publishing<\/td>\n<\/tr>\n<tr>\n<td>ISBN10:<\/td>\n<td>1800564481<\/td>\n<\/tr>\n<tr>\n<td>ISBN13:<\/td>\n<td>9781800564480<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628<\/h2>\n<div style=\"direction: ltr;\">Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost<br \/>\nKey FeaturesThink critically about data and use it to form and test a hypothesis<br \/>\nChoose an appropriate machine learning model and train it on your data<br \/>\nCommunicate data-driven insights with confidence and clarity<\/p>\n<p>Book Description<\/p>\n<p>If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.<\/p>\n<p>In this book, you\u2019ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you\u2019ll experience in real-world data science projects.<\/p>\n<p>You\u2019ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.<\/p>\n<p>Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.<\/p>\n<p>By the end of this data science book, you\u2019ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.<br \/>\nWhat you will learn<\/p>\n<p>Load, explore, and process data using the pandas Python package<br \/>\nUse Matplotlib to create compelling data visualizations<br \/>\nImplement predictive machine learning models with scikit-learn<br \/>\nUse lasso and ridge regression to reduce model overfitting<br \/>\nEvaluate random forest and logistic regression model performance<br \/>\nDeliver business insights by presenting clear, convincing conclusions<\/p>\n<p>Who this book is for<\/p>\n<p>Data Science Projects with Python \u2013 Second Edition is for anyone who wants to get started with data science and machine learning. If you\u2019re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.<br \/>\nTable of Contents<\/p>\n<p>Data Exploration and Cleaning<br \/>\nIntroduction to Scikit-Learn and Model Evaluation<br \/>\nDetails of Logistic Regression and Feature Exploration<br \/>\nThe Bias-Variance Trade-off<br \/>\nDecision Trees and Random Forests<br \/>\nGradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values<br \/>\nTest Set Analysis, Financial Insights, and Delivery to the Client<\/p>\n<\/div>\n<h2>\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<p>\u062a\u062c\u0631\u0628\u0647 \u062f\u0633\u062a\u06cc \u0627\u0632 \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 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\u0628\u0627 \u0634\u0645\u0627 \u0628\u0627\u0634\u062f.\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\t\r\n\t<tr>\r\n\t\t<td>\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<br \/><br \/>\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<br \/>\r\n\r\n<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; \u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc: Data Science Projects with Python: A case study approach to gaining valuable insights from real [&hellip;]<\/p>\n","protected":false},"featured_media":26897,"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 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