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{ "item_title" : "AI & ML - Theoretical Approach", "item_author" : [" Deepak Gupta", "Shyam Gupta "], "item_description" : "Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. The chapter introduces the history of AI, its key components, and various types including narrow AI and general AI. It discusses the significance of AI in modern technology and its potential impact on various industries. Machine Learning (ML) is a subset of AI that involves the use of algorithms and statistical models to enable computers to perform specific tasks without using explicit instructions. This chapter covers the basics of ML, including supervised, unsupervised, and reinforcement learning. It also highlights the importance of data in ML and the different types of algorithms used in machine learning.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/6/20/780/546/6207805461_b.jpg", "price_data" : { "retail_price" : "92.00", "online_price" : "92.00", "our_price" : "92.00", "club_price" : "92.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
AI & ML - Theoretical Approach|Deepak Gupta

AI & ML - Theoretical Approach

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Overview

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. The chapter introduces the history of AI, its key components, and various types including narrow AI and general AI. It discusses the significance of AI in modern technology and its potential impact on various industries. Machine Learning (ML) is a subset of AI that involves the use of algorithms and statistical models to enable computers to perform specific tasks without using explicit instructions. This chapter covers the basics of ML, including supervised, unsupervised, and reinforcement learning. It also highlights the importance of data in ML and the different types of algorithms used in machine learning.

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Details

  • ISBN-13: 9786207805464
  • ISBN-10: 6207805461
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: June 2024
  • Dimensions: 9 x 6 x 0.51 inches
  • Shipping Weight: 0.74 pounds
  • Page Count: 224

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