diff --git a/docs/exercises/06_classification.ipynb b/docs/exercises/06_classification.ipynb
index fecbee6..7d4668f 100644
--- a/docs/exercises/06_classification.ipynb
+++ b/docs/exercises/06_classification.ipynb
@@ -932,14 +932,15 @@
     "\n",
     "Support vector machines work by finding the hyperplane that best separates the classes in the feature space. The hyperplane is defined by the support vectors, which are the points that are closest to the hyperplane. The distance between the support vectors and the hyperplane is called the margin. The goal of the SVM is to maximize the margin, which is done by minimizing the norm of the weight vector. The SVM can be used for both linear and non-linear classification by using different kernel functions.\n",
     "\n",
-    "![](images/SVM-basic.png)\n",
+    "<!-- ![](images/SVM-basic.png) -->\n",
+    "<img src=\"images/SVM-basic.png\" >\n",
     "\n",
     "## What is a kernel function?\n",
     "\n",
     "A kernel function is a function that takes two input vectors and returns a scalar value. The kernel function is used to map the input vectors into a higher-dimensional space where the classes are linearly separable. The most common kernel functions are the linear kernel, the polynomial kernel, and the radial basis function (RBF) kernel.\n",
     "\n",
-    "![](images/SVM-kernel.png)\n",
-    "\n",
+    "<!-- ![](images/SVM-kernel.png) -->\n",
+    "<img src=\"images/SVM-kernel.png\" >\n",
     "\n"
    ]
   },