diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index b30699cf3df8615b0e9360a751849aed76da3342..f5788dc46776d6709f1991195aefe2e742a35841 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -83,7 +83,7 @@ DStreams support many of the transformations available on normal Spark RDD's:
 <tr>
   <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
   <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together all the values of each key in the RDDs of the source DStream. <br />
-  <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+  <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
 </td>
 </tr>
 <tr>
@@ -132,7 +132,7 @@ Spark Streaming features windowed computations, which allow you to apply transfo
   <td> <b>groupByKeyAndWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>])
   </td>
   <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together values of each key over batches in a sliding window. <br />
-<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
+<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
 </tr>
 <tr>
   <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td>