000			01192nam a22001817a 4500
003			OSt
005			20250528153955.0
008			250528b |||||||| |||| 00| 0 eng d
020			_a9789355515636
082			_a006.31 _bBHA
100			_aBhasin, Harsh
245			_aMachine Learning for Beginners
250			_a2nd
260			_aNew Delhi _bBPB _c2025
300			_axxiii, 360p. _c24 x 18 cm
520			_aSection I: Fundamentals 1. An Introduction to Machine Learning 2. The Beginning: Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section II: Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Classification: Logistic Regression and Naïve Bayes Classifier 9. Neural Network I: The Perceptron 10. Neural Network II: The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section III: Unsupervised Learning and Deep Learning 14. Clustering 15. Deep Learning Appendix 1: Glossary Appendix 2: Methods/Techniques Appendix 3: Important Metrics and Formulas Appendix 4: Visualization- Matplotlib Answers to Multiple Choice Questions Bibliography
942			_2ddc _cREF
999			_c6366 _d6366