diff --git a/core/src/main/java/org/apache/spark/TaskContext.java b/core/src/main/java/org/apache/spark/TaskContext.java
index 2d998d4c7a5d94b78f01873a09ac66803784033f..0d6973203eba17dd168bc92d4e7a6acbd7226939 100644
--- a/core/src/main/java/org/apache/spark/TaskContext.java
+++ b/core/src/main/java/org/apache/spark/TaskContext.java
@@ -71,7 +71,6 @@ public abstract class TaskContext implements Serializable {
   /**
    * Add a (Java friendly) listener to be executed on task completion.
    * This will be called in all situation - success, failure, or cancellation.
-   * <p/>
    * An example use is for HadoopRDD to register a callback to close the input stream.
    */
   public abstract TaskContext addTaskCompletionListener(TaskCompletionListener listener);
@@ -79,7 +78,6 @@ public abstract class TaskContext implements Serializable {
   /**
    * Add a listener in the form of a Scala closure to be executed on task completion.
    * This will be called in all situations - success, failure, or cancellation.
-   * <p/>
    * An example use is for HadoopRDD to register a callback to close the input stream.
    */
   public abstract TaskContext addTaskCompletionListener(final Function1<TaskContext, Unit> f);
diff --git a/core/src/main/java/org/apache/spark/api/java/function/PairFunction.java b/core/src/main/java/org/apache/spark/api/java/function/PairFunction.java
index abd9bcc07ac61a67404de69bbb765d298f7f221d..99bf240a172254ef8e812e060cdc6f507279ef8a 100644
--- a/core/src/main/java/org/apache/spark/api/java/function/PairFunction.java
+++ b/core/src/main/java/org/apache/spark/api/java/function/PairFunction.java
@@ -22,7 +22,8 @@ import java.io.Serializable;
 import scala.Tuple2;
 
 /**
- * A function that returns key-value pairs (Tuple2<K, V>), and can be used to construct PairRDDs.
+ * A function that returns key-value pairs (Tuple2&lt;K, V&gt;), and can be used to
+ * construct PairRDDs.
  */
 public interface PairFunction<T, K, V> extends Serializable {
   public Tuple2<K, V> call(T t) throws Exception;
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
index a6123bd108c116dd2ae02bf9d2c50ffb5258d005..8e8f7f6c4fda2cb470b2e247605c4bbe5281bd22 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
@@ -114,7 +114,7 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, Ja
    * Return an RDD with the elements from `this` that are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be &lt;= us.
    */
   def subtract(other: JavaDoubleRDD): JavaDoubleRDD =
     fromRDD(srdd.subtract(other))
@@ -233,11 +233,11 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, Ja
    * to the left except for the last which is closed
    *  e.g. for the array
    *  [1,10,20,50] the buckets are [1,10) [10,20) [20,50]
-   *  e.g 1<=x<10 , 10<=x<20, 20<=x<50
+   *  e.g 1&lt;=x&lt;10 , 10&lt;=x&lt;20, 20&lt;=x&lt;50
    *  And on the input of 1 and 50 we would have a histogram of 1,0,0
    *
    * Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched
-   * from an O(log n) inseration to O(1) per element. (where n = # buckets) if you set evenBuckets
+   * from an O(log n) insertion to O(1) per element. (where n = # buckets) if you set evenBuckets
    * to true.
    * buckets must be sorted and not contain any duplicates.
    * buckets array must be at least two elements
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index c38b96528d0377b4e89cddd9ce70ee67af4eaa51..e37f3acaf6e30c8b737545fba82d9d5be1833f50 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -392,7 +392,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return an RDD with the elements from `this` that are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be &lt;= us.
    */
   def subtract(other: JavaPairRDD[K, V]): JavaPairRDD[K, V] =
     fromRDD(rdd.subtract(other))
@@ -413,7 +413,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return an RDD with the pairs from `this` whose keys are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be &lt;= us.
    */
   def subtractByKey[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, V] = {
     implicit val ctag: ClassTag[W] = fakeClassTag
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 45168ba62d3c1a751505ef2db1ee27ffc611077a..0565adf4d4ead16f102326d1ae3c1129e3373b5b 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -215,7 +215,10 @@ class JavaSparkContext(val sc: SparkContext)
    *   hdfs://a-hdfs-path/part-nnnnn
    * }}}
    *
-   * Do `JavaPairRDD<String, String> rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")`,
+   * Do
+   * {{{
+   *   JavaPairRDD<String, String> rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")
+   * }}}
    *
    * <p> then `rdd` contains
    * {{{
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 4734251127bb407629d5ed00f56f9b80637cffd7..dfad25d57c947253cd94246156d104523afc7d6a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -26,7 +26,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  * :: Experimental ::
  * Normalizes samples individually to unit L^p^ norm
  *
- * For any 1 <= p < Double.PositiveInfinity, normalizes samples using
+ * For any 1 &lt;= p &lt; Double.PositiveInfinity, normalizes samples using
  * sum(abs(vector).^p^)^(1/p)^ as norm.
  *
  * For p = Double.PositiveInfinity, max(abs(vector)) will be used as norm for normalization.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index ec2d481dccc220b6ca8d4e8b6def096352e359c6..10a515af888023ded03fb4db97118b28378b320d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -152,7 +152,7 @@ class RowMatrix(
    * storing the right singular vectors, is computed via matrix multiplication as
    * U = A * (V * S^-1^), if requested by user. The actual method to use is determined
    * automatically based on the cost:
-   *  - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian
+   *  - If n is small (n &lt; 100) or k is large compared with n (k > n / 2), we compute the Gramian
    *    matrix first and then compute its top eigenvalues and eigenvectors locally on the driver.
    *    This requires a single pass with O(n^2^) storage on each executor and on the driver, and
    *    O(n^2^ k) time on the driver.
@@ -169,7 +169,8 @@ class RowMatrix(
    * @note The conditions that decide which method to use internally and the default parameters are
    *       subject to change.
    *
-   * @param k number of leading singular values to keep (0 < k <= n). It might return less than k if
+   * @param k number of leading singular values to keep (0 &lt; k &lt;= n).
+   *          It might return less than k if
    *          there are numerically zero singular values or there are not enough Ritz values
    *          converged before the maximum number of Arnoldi update iterations is reached (in case
    *          that matrix A is ill-conditioned).
@@ -192,7 +193,7 @@ class RowMatrix(
   /**
    * The actual SVD implementation, visible for testing.
    *
-   * @param k number of leading singular values to keep (0 < k <= n)
+   * @param k number of leading singular values to keep (0 &lt; k &lt;= n)
    * @param computeU whether to compute U
    * @param rCond the reciprocal condition number
    * @param maxIter max number of iterations (if ARPACK is used)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index ca35100aa99c650dbc024f1419c908071f60d89b..dce0adffa6249668d55249d62cba04f86506e960 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -196,8 +196,8 @@ object MLUtils {
 
   /**
    * Load labeled data from a file. The data format used here is
-   * <L>, <f1> <f2> ...
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   * L, f1 f2 ...
+   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
    *
    * @param sc SparkContext
    * @param dir Directory to the input data files.
@@ -219,8 +219,8 @@ object MLUtils {
 
   /**
    * Save labeled data to a file. The data format used here is
-   * <L>, <f1> <f2> ...
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   * L, f1 f2 ...
+   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
    *
    * @param data An RDD of LabeledPoints containing data to be saved.
    * @param dir Directory to save the data.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index e7faba0c7f6204e7091b597eb25c655ab8d2ac7d..1e0ccb368a2767b1475464604784d2a91c25b5d8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -193,7 +193,7 @@ class JavaSchemaRDD(
    * Return an RDD with the elements from `this` that are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be &lt;= us.
    */
   def subtract(other: JavaSchemaRDD): JavaSchemaRDD =
     this.baseSchemaRDD.subtract(other.baseSchemaRDD).toJavaSchemaRDD