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diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
+from pyspark.ml.util import inherit_doc
+from pyspark.ml.wrapper import JavaTransformer
+
+__all__ = ['Tokenizer', 'HashingTF']
+
+
+@inherit_doc
+class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A tokenizer that converts the input string to lowercase and then
+    splits it by white spaces.
+
+    >>> from pyspark.sql import Row
+    >>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(text="a b c")]))
+    >>> tokenizer = Tokenizer() \
+            .setInputCol("text") \
+            .setOutputCol("words")
+    >>> print tokenizer.transform(dataset).head()
+    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    >>> print tokenizer.transform(dataset, {tokenizer.outputCol: "tokens"}).head()
+    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.Tokenizer"
+
+
+@inherit_doc
+class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
+    """
+    Maps a sequence of terms to their term frequencies using the
+    hashing trick.
+
+    >>> from pyspark.sql import Row
+    >>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(words=["a", "b", "c"])]))
+    >>> hashingTF = HashingTF() \
+            .setNumFeatures(10) \
+            .setInputCol("words") \
+            .setOutputCol("features")
+    >>> print hashingTF.transform(dataset).head().features
+    (10,[7,8,9],[1.0,1.0,1.0])
+    >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
+    >>> print hashingTF.transform(dataset, params).head().vector
+    (5,[2,3,4],[1.0,1.0,1.0])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.HashingTF"
+
+
+if __name__ == "__main__":
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.feature tests")
+    sqlCtx = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlCtx'] = sqlCtx
+    (failure_count, test_count) = doctest.testmod(
+        globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)