Update README

easy/Linear_Kernel/README.md

@@ -40,9 +40,7 @@ A kernel function in machine learning is used to measure the similarity between
 
 The linear kernel between two vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ is mathematically defined as:
 
-$$
-K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i}
-$$
+$K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i}$
 
 Where $n$ is the number of features, and $x_{1,i}$ and $x_{2,i}$ are the components of the vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ respectively.
 
@@ -113,6 +111,6 @@ Both NumPy functions are more efficient and can handle multi-dimensional arrays,
 
 To ensure proper rendering of LaTeX equations in various Markdown environments, we'll use inline LaTeX notation. The linear kernel between two vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ is mathematically defined as:
 
-$K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i}$
+
 
 Where $n$ is the number of features, and $x_{1,i}$ and $x_{2,i}$ are the components of the vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ respectively.