diff --git a/ProgrammingAssignment_0/GettingFamiliar.ipynb b/ProgrammingAssignment_0/GettingFamiliar.ipynb
index 04f5b47575a70be27c40d4043545b6bcbca8483a..dcdcb16f14696dd665b4cfb2757c3a71256bd98a 100644
--- a/ProgrammingAssignment_0/GettingFamiliar.ipynb
+++ b/ProgrammingAssignment_0/GettingFamiliar.ipynb
@@ -9,7 +9,7 @@
     "Each rectangular box is called a cell. \n",
     "* Ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.\n",
     "* Alt+ENTER evaluates the current cell and adds a new cell below it.\n",
-    "* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two \"d\"s in a row) when the frame is blue."
+    "* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two **d**s in a row) when the frame is blue."
    ]
   },
   {
@@ -44,14 +44,14 @@
    "source": [
     "# Supervised Learning Model Skeleton\n",
     "\n",
-    "We'll use this skeleton for implementing different supervised learning algorithms. For this first assignment, we'll read and partition the [\"madelon\" dataset](http://archive.ics.uci.edu/ml/datasets/madelon). Features and labels for the first two examples are listed below. Please complete \"preprocess\" and \"partition\" functions. "
+    "We'll use this skeleton for implementing different supervised learning algorithms. For this first assignment, we'll read and partition the [**madelon** dataset](http://archive.ics.uci.edu/ml/datasets/madelon). Features and labels for the first two examples are listed below. Please complete **preprocess** and **partition** functions."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The 500 features in the \"madelon\" dataset have integer values:"
+    "The 500 features in the **madelon** dataset have integer values:"
    ]
   },
   {
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This step is for reading the dataset and for extracting features and labels. The \"preprocess\" function should return an *n x d* \"features\" array, and an *n x 1* \"labels\" array, where *n* is the number of examples and *d* is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization."
+    "This step is for reading the dataset and for extracting features and labels. The **preprocess** function should return an *n x d* **features** array, and an *n x 1* **labels** array, where *n* is the number of examples and *d* is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization."
    ]
   },
   {
@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, you'll need to split your dataset into training, validation and test sets. The \"partition\" function should take as input the size of the whole dataset and randomly sample a proportion *t* of the dataset indices for test partition and a proportion of *v* for validation partition. The remaining will be used as indices for training data. For example, to keep 30% of the examples as test and %10 as validation, set *t* = 0.3 and *v* = 0.1. You should choose these values according to the size of the data available to you. The \"split\" function should return indices of the training, validation and test sets. These will be used to index into the whole training set."
+    "Next, you'll need to split your dataset into training, validation and test sets. The **partition** function should take as input the size of the whole dataset and randomly sample a proportion *t* of the dataset indices for test partition and a proportion of *v* for validation partition. The remaining will be used as indices for training data. For example, to keep 30% of the examples as test and %10 as validation, set *t* = 0.3 and *v* = 0.1. You should choose these values according to the size of the data available to you. The **split** function should return indices of the training, validation and test sets. These will be used to index into the whole training set."
    ]
   },
   {
@@ -260,7 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The model definition is given below. We'll extend this class for different supervised classification algorithms. Specifically, we'll implement \"fit\" and \"predict\" methods for these algorithms. For this assignment, you are not asked to implement these methods. Run the cells below and make sure each piece of code fits together and works as expected."
+    "The model definition is given below. We'll extend this class for different supervised classification algorithms. Specifically, we'll implement **fit** and **predict** methods for these algorithms. For this assignment, you are not asked to implement these methods. Run the cells below and make sure each piece of code fits together and works as expected."
    ]
   },
   {