diff --git a/lab4_machine_learning_student.ipynb b/lab4_machine_learning_student.ipynb
index 842161b..a5a8d65 100644
--- a/lab4_machine_learning_student.ipynb
+++ b/lab4_machine_learning_student.ipynb
@@ -495,7 +495,7 @@
    "source": [
     "#### **(2b) Root Mean Square Error (RMSE)**\n",
     "#### In the next part, you will generate a few different models, and will need a way to decide which model is best. We will use the [Root Mean Square Error](https://en.wikipedia.org/wiki/Root-mean-square_deviation) (RMSE) or Root Mean Square Deviation (RMSD) to compute the error of each model.  RMSE is a frequently used measure of the differences between values (sample and population values) predicted by a model or an estimator and the values actually observed. The RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.\n",
-    "####  The RMSE is the square root of the average value of the square of `(actual rating - predicted rating)` for all users and movies for which we have the actual rating. Versions of Spark MLlib beginning with Spark 1.4 include a [RegressionMetrics](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.evaluation.RegressionMetrics) modiule that can be used to compute the RMSE. However, since we are using Spark 1.3.1, we will write our own function.\n",
+    "####  The RMSE is the square root of the average value of the square of `(actual rating - predicted rating)` for all users and movies for which we have the actual rating. Versions of Spark MLlib beginning with Spark 1.4 include a [RegressionMetrics](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.evaluation.RegressionMetrics) module that can be used to compute the RMSE. However, since we are using Spark 1.3.1, we will write our own function.\n",
     "#### Write a function to compute the sum of squared error given `predictedRDD` and `actualRDD` RDDs. Both RDDs consist of tuples of the form (UserID, MovieID, Rating)\n",
     "#### Given two ratings RDDs, *x* and *y* of size *n*, we define RSME as follows: $ RMSE = \\sqrt{\\frac{\\sum_{i = 1}^{n} (x_i - y_i)^2}{n}}$\n",
     "#### To calculate RSME, the steps you should perform are:\n",