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{ "item_title" : "Machine Learning Algorithms Using Scikit and TensorFlow Environments", "item_author" : [" Puvvadi Baby Maruthi", "Smrity Prasad", "Amit Kumar Tyagi "], "item_description" : "Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students. ", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/66/848/531/1668485311_b.jpg", "price_data" : { "retail_price" : "300.00", "online_price" : "300.00", "our_price" : "300.00", "club_price" : "300.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning Algorithms Using Scikit and TensorFlow Environments|Puvvadi Baby Maruthi

Machine Learning Algorithms Using Scikit and TensorFlow Environments

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Overview

Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

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Details

  • ISBN-13: 9781668485316
  • ISBN-10: 1668485311
  • Publisher: IGI Global
  • Publish Date: December 2023
  • Dimensions: 11 x 8.5 x 1.06 inches
  • Shipping Weight: 3.07 pounds
  • Page Count: 320

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