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diff --git a/src/ml/dlib/python_examples/svm_binary_classifier.py b/src/ml/dlib/python_examples/svm_binary_classifier.py
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+#!/usr/bin/python
+# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+#
+# 
+# This is an example illustrating the use of a binary SVM classifier tool from
+# the dlib C++ Library.  In this example, we will create a simple test dataset
+# and show how to learn a classifier from it.  
+#
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
+#
+
+import dlib
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+x = dlib.vectors()
+y = dlib.array()
+
+# Make a training dataset.  Here we have just two training examples.  Normally
+# you would use a much larger training dataset, but for the purpose of example
+# this is plenty.  For binary classification, the y labels should all be either +1 or -1.
+x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
+y.append(+1)
+
+x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
+y.append(-1)
+
+
+# Now make a training object.  This object is responsible for turning a
+# training dataset into a prediction model.  This one here is a SVM trainer
+# that uses a linear kernel.  If you wanted to use a RBF kernel or histogram
+# intersection kernel you could change it to one of these lines:
+#  svm = dlib.svm_c_trainer_histogram_intersection()
+#  svm = dlib.svm_c_trainer_radial_basis()
+svm = dlib.svm_c_trainer_linear()
+svm.be_verbose()
+svm.set_c(10)
+
+# Now train the model.  The return value is the trained model capable of making predictions.
+classifier = svm.train(x, y)
+
+# Now run the model on our data and look at the results.
+print("prediction for first sample:  {}".format(classifier(x[0])))
+print("prediction for second sample: {}".format(classifier(x[1])))
+
+
+# classifier models can also be pickled in the same was as any other python object.
+with open('saved_model.pickle', 'wb') as handle:
+    pickle.dump(classifier, handle, 2)
+