Fixed setence in ex1 and comment in ex3

Exercise1/exercise1.ipynb

@@ -192,7 +192,7 @@
     "\n",
     "Now you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next. \n",
     "\n",
-    "The file `Data/ex1data1.txt` contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss. \n",
+    "The file `Data/ex1data1.txt` contains the dataset for our linear regression problem. The first column is the population of a city (in 10,000s) and the second column is the profit of a food truck in that city (in $10,000s). A negative value for profit indicates a loss. \n",
     "\n",
     "We provide you with the code needed to load this data. The dataset is loaded from the data file into the variables `x` and `y`:"
    ]

Exercise3/exercise3.ipynb

@@ -505,7 +505,7 @@
     "        initial_theta = np.zeros(n + 1)\n",
     "      \n",
     "        # Set options for minimize\n",
-    "        options = {'maxiter', 50}\n",
+    "        options = {'maxiter': 50}\n",
     "    \n",
     "        # Run minimize to obtain the optimal theta. This function will \n",
     "        # return a class object where theta is in `res.x` and cost in `res.fun`\n",
@@ -513,7 +513,7 @@
     "                                initial_theta, \n",
     "                                (X, (y == c), lambda_), \n",
     "                                jac=True, \n",
-    "                                method='TNC')\n",
+    "                                method='TNC',\n",
     "                                options=options) \n",
     "    \"\"\"\n",
     "    # Some useful variables\n",