menu
{ "item_title" : "Introduction to Algorithms for Data Mining and Machine Learning", "item_author" : [" Xin-She Yang "], "item_description" : "Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/0/12/817/216/0128172169_b.jpg", "price_data" : { "retail_price" : "74.95", "online_price" : "74.95", "our_price" : "74.95", "club_price" : "74.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Introduction to Algorithms for Data Mining and Machine Learning|Xin-She Yang

Introduction to Algorithms for Data Mining and Machine Learning

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

This item is Non-Returnable

Details

  • ISBN-13: 9780128172162
  • ISBN-10: 0128172169
  • Publisher: Academic Press
  • Publish Date: June 2019
  • Dimensions: 9 x 6 x 0.4 inches
  • Shipping Weight: 0.57 pounds
  • Page Count: 188

Related Categories

You May Also Like...

    1

BAM Customer Reviews