DDE-1/Regression.md

@@ -31,13 +31,10 @@ SVM is now one of the mostly applied techniques in supervised machine learning t
 In order to understand how it works, let’s go back to its origins and imagine a binary classification task (reds and blues) in 2D data space, which is linearly separable. In other words, it means that there exists at least one line that can separate these points. If we try to a pencil, we see however that may lines do exist and the million-dollar question here is to figure out which line (a hyperplane in N dimensional case) is the best way to separate. 
 For mathematical convenience, let’s go in to the number domain, rather than sticking to colours, and say that we are trying to separate positive numbers from the negative numbers. In this case, the decision boundary corresponds to the locations of zeros in this special line:
 
-```math 
-y_{i} = w . x_i + b >= 0 for positive points
-```
-```math 
-y_{i} = w . x_i + b <= 0 for negative points
-```
+$`y_{i} = w . x_i + b >= 0`$ for positive points 
+$`y_{i} = w . x_i + b <= 0`$ for negative points
 
+...
 
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