diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index abbc1c1..ef7c39f 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -869,7 +869,7 @@
     "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
     "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
     "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
-    "    ax.set_zlabel(r\"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$\", fontsize=18)\n",
+    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18)\n",
     "    ax.legend(loc=\"upper left\", fontsize=16)\n",
     "\n",
     "fig = plt.figure(figsize=(11, 6))\n",
@@ -1165,7 +1165,7 @@
    "source": [
     "from sklearn.linear_model import SGDClassifier\n",
     "\n",
-    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, n_iter = 50, random_state=42)\n",
+    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, max_iter = 50, random_state=42)\n",
     "sgd_clf.fit(X, y.ravel())\n",
     "\n",
     "m = len(X)\n",
@@ -1235,9 +1235,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn import datasets\n",
@@ -1267,7 +1265,7 @@
     "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
     "svm_clf = SVC(kernel=\"linear\", C=C)\n",
     "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
-    "                        n_iter=100000, random_state=42)\n",
+    "                        max_iter=100000, random_state=42)\n",
     "\n",
     "scaler = StandardScaler()\n",
     "X_scaled = scaler.fit_transform(X)\n",
@@ -1378,9 +1376,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed(42)\n",
@@ -1605,9 +1601,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.datasets import fetch_california_housing\n",
@@ -1627,9 +1621,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -1647,9 +1639,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
@@ -1784,9 +1774,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -1807,7 +1795,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.5.2"
   },
   "nav_menu": {},
   "toc": {