LEADER 03075nam 22004933 450 001 9910984669403321 005 20230918084512.0 010 $a9789815136982 010 $a9815136984 035 $a(CKB)28153922900041 035 $a(MiAaPQ)EBC30745091 035 $a(Au-PeEL)EBL30745091 035 $a(Exl-AI)30745091 035 $a(OCoLC)1399170449 035 $a(EXLCZ)9928153922900041 100 $a20230918d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNumerical Machine Learning 205 $a1st ed. 210 1$aSharjah :$cBentham Science Publishers,$d2023. 210 4$dİ2023. 215 $a1 online resource (225 pages) 311 08$a9789815136999 311 08$a9815136992 327 $aCover -- Title -- Copyright -- End User License Agreement -- Content -- Preface -- Introduction to Machine Learning -- Linear Regression -- Regularization -- Logistic Regression -- Decision Tree -- Gradient Boosting -- Support Vector Machine -- K-means Clustering -- Subject Index $7Generated by AI. 330 $aNumerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses. 606 $aMachine learning$7Generated by AI 606 $aNumerical analysis$7Generated by AI 615 0$aMachine learning 615 0$aNumerical analysis 700 $aWang$b Zhiyuan$0654347 701 $aIrfan$b Sayed Ameenuddin$01793401 701 $aTeoh$b Christopher$01793402 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910984669403321 996 $aNumerical Machine Learning$94333097 997 $aUNINA