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diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.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 numpy import array, dot, shape
+from pyspark import SparkContext
+from pyspark.mllib._common import \
+    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
+    _serialize_double_matrix, _deserialize_double_matrix, \
+    _serialize_double_vector, _deserialize_double_vector, \
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
+    LinearModel, _linear_predictor_typecheck
+from math import exp, log
+
+class LogisticRegressionModel(LinearModel):
+    """A linear binary classification model derived from logistic regression.
+
+    >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
+    >>> lrm = LogisticRegressionWithSGD.train(sc, sc.parallelize(data))
+    >>> lrm.predict(array([1.0])) != None
+    True
+    """
+    def predict(self, x):
+        _linear_predictor_typecheck(x, self._coeff)
+        margin = dot(x, self._coeff) + self._intercept
+        prob = 1/(1 + exp(-margin))
+        return 1 if prob > 0.5 else 0
+
+class LogisticRegressionWithSGD(object):
+    @classmethod
+    def train(cls, sc, data, iterations=100, step=1.0,
+              mini_batch_fraction=1.0, initial_weights=None):
+        """Train a logistic regression model on the given data."""
+        return _regression_train_wrapper(sc, lambda d, i:
+                sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
+                        iterations, step, mini_batch_fraction, i),
+                LogisticRegressionModel, data, initial_weights)
+
+class SVMModel(LinearModel):
+    """A support vector machine.
+
+    >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
+    >>> svm = SVMWithSGD.train(sc, sc.parallelize(data))
+    >>> svm.predict(array([1.0])) != None
+    True
+    """
+    def predict(self, x):
+        _linear_predictor_typecheck(x, self._coeff)
+        margin = dot(x, self._coeff) + self._intercept
+        return 1 if margin >= 0 else 0
+
+class SVMWithSGD(object):
+    @classmethod
+    def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
+              mini_batch_fraction=1.0, initial_weights=None):
+        """Train a support vector machine on the given data."""
+        return _regression_train_wrapper(sc, lambda d, i:
+                sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
+                        iterations, step, reg_param, mini_batch_fraction, i),
+                SVMModel, data, initial_weights)
+
+def _test():
+    import doctest
+    globs = globals().copy()
+    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
+    (failure_count, test_count) = doctest.testmod(globs=globs,
+            optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()