diff --git a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
index 62d7633202aea1bc6377448376d54f1f0b250b19..60b7992745ddac818bc5d878b136409b069fe2c8 100644
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
@@ -341,56 +341,7 @@
     "print('Confidence interval: {}-{}'.format(lower_bound, upper_bound))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ## TASK 4: Plotting a learning curve\n",
-    " \n",
-    "A learning curve shows how error changes as the training set size increases. For more information, see [learning curves](https://www.dataquest.io/blog/learning-curves-machine-learning/).\n",
-    "We'll plot the error values for training and validation data while varying the size of the training set. Report a good size for training set for which there is a good balance between bias and variance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rubric:\n",
-    "* Correct training error calculation for different training set sizes +8, +8\n",
-    "* Correct validation error calculation for different training set sizes +8, +8\n",
-    "* Reasonable learning curve +4, +4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# train using %10, %20, %30, ..., 100% of training data\n",
-    "training_proportions = np.arange(0.10, 1.01, 0.10)\n",
-    "train_size = len(train_indices)\n",
-    "training_sizes = np.int(np.ceil(train_size*proportion))\n",
-    "\n",
-    "# TODO\n",
-    "error_train = []\n",
-    "error_val = []\n",
-    "\n",
-    "# For each size in training_sizes\n",
-    "for size in training_sizes:\n",
-    "    # fit the model using \"size\" data point\n",
-    "    # Calculate error for training and validation sets\n",
-    "    # populate error_train and error_val arrays. \n",
-    "    # Each entry in these arrays\n",
-    "    # should correspond to each entry in training_sizes.\n",
-    "\n",
-    "# plot the learning curve\n",
-    "plt.plot(training_sizes, error_train, 'r', label = 'training_error')\n",
-    "plt.plot(training_sizes, error_val, 'g', label = 'validation_error')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
+  
   {
    "cell_type": "markdown",
    "metadata": {},