000			a
999			_c33757 _d33757
008			250228b xxu||||| |||| 00| 0 eng d
020			_a9783031469893 _c(hbk)
082			_a006.31 _bHOS
100			_aHossain, Eklas
245			_aMachine learning crash course for engineers
260			_bSpringer, _c2024 _aCham :
300			_axx, 453 p. ; _bill., (chiefly col.), _c25 cm
365			_b59.99 _c€ _d93.20
504			_aIncludes bibliographical references and index.
520			_aMachine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly. A concise guide to the basics of algorithms, building models, and performance evaluation; Offers highly illustrated, step-by-step guidelines with Python programming examples; Provides examples and exercises related to signal and image processing, energy systems, and robotics.
650			_aChebyshev distance
650			_aComputer vision
650			_aCosine similarity
650			_aData points
650			_aEuclidean distance
650			_aHamming distance
650			_aLoss function
650			_aManhattan distance
650			_aRandom forest
650			_aTrain Epoch
942			_2ddc _cBK