000			01284aam a2200205 4500
003			OSt
005			20230121124642.0
008			221123b |||||||| |||| 00| 0 eng d
020			_a9781138495685
040			_c
082			_a006.31 _bBOE
100			_aBoehmke, Brad _963247
245			_aHands-on Machine Learning with R
260			_bCRC Press _c2020 _aBoca Raton
300			_a459p
500			_aI FUNDAMENTALS 1. Introduction to Machine Learning 2. Modeling Process 3. Feature & Target Engineering II SUPERVISED LEARNING 4. Linear Regression 5. Logistic Regression 6. Regularized 7. Multivariate Adaptive Regression Splines 8. K-Nearest Neighbors 9 Decision Trees 10. Bagging 11. Random Forests 12. Gradient Boosting 1 13. Deep Learning 13.1 Prerequisites 14. Support Vector Machines 15. Stacked Models 16. Interpretable Machine Learning III DIMENSION REDUCTION 17. Principal Components Analysis 18. Generalized Low Rank Models 19. Auto encoders IV Clustering 20. K-means Clustering 21. Hierarchical Clustering 22. Model-based Clustering
600			_aMachine Learning - R (Computer Program Language) _964346
700			_aGreenwell, Brandon _964347
942			_2ddc _cLB _k006.31 _mBOE
999			_c139315 _d139315