Soft & Hard Margin Support Vector Machine (SVM)| Machine Learning with TensorFlow & scikit-learn

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This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning model associated with learning algorithms that analyze data used for classification and regression analysis. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997), it presents one of the most robust prediction methods, based on the statistical learning framework or VC theory proposed by Vapnik and Chervonenkis (1974) and Vapnik (1982, 1995).

⏲Outline

00:00:00 Introduction
00:01:11 Support Vector Machines
00:03:55 Supporting Vectors and Hyperplanes
00:07:05 SVM Mathematical Modelling
00:08:58 Hard Margin SVM
00:47:21 Outlier Sensitivity & Linear Separability
00:49:11 Hard Margin SVM on Python
01:13:15 Soft Margin SVM
01:27:09 Soft Margin SVM on Python
01:31:47 Outro

Ahmad Bazzi

Ahmad Bazzi is an Electrical Engineer and YouTuber. Ahmad Bazzi is a signal processing engineer at CEVA DSP. He holds a PhD in Electrical Engineering from EURECOM. Ahmad Bazzi is a EURECOM alumni. Ahmad Bazzi obtained his Master studies (Summa Cum Laude) in Electrical Engineering at SUPELEC, Paris. Ahmad Bazzi has many publications in well-known IEEE conferences, including a nomination award and is a co-inventor in several patents. Ahmad Bazzi dedicates time to publish high level lectures on Mathematics (including convex optimization) and Programming. Ahmad Bazzi also focuses on Machine Learning, Convex Optimization, Linear Algebra, Python, SymPy, NumPy, Pandas, CVXOPT, MATLAB, C++ for firmware systems.

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