diff --git a/samples/python2/digits.py b/samples/python2/digits.py
index 63dbb1e9d8..a0ea337b70 100755
--- a/samples/python2/digits.py
+++ b/samples/python2/digits.py
@@ -74,30 +74,30 @@ class StatModel(object):
 class KNearest(StatModel):
     def __init__(self, k = 3):
         self.k = k
-        self.model = cv2.KNearest()
+        self.model = cv2.ml.KNearest_create()
 
     def train(self, samples, responses):
-        self.model = cv2.KNearest()
-        self.model.train(samples, responses)
+        self.model = cv2.ml.KNearest_create()
+        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
+        retval, results, neigh_resp, dists = self.model.findNearest(samples, self.k)
         return results.ravel()
 
 class SVM(StatModel):
     def __init__(self, C = 1, gamma = 0.5):
-        self.params = dict( kernel_type = cv2.SVM_RBF,
-                            svm_type = cv2.SVM_C_SVC,
-                            C = C,
-                            gamma = gamma )
-        self.model = cv2.SVM()
+        self.model = cv2.ml.SVM_create()
+        self.model.setGamma(gamma)
+        self.model.setC(C)
+        self.model.setKernel(cv2.ml.SVM_RBF)
+        self.model.setType(cv2.ml.SVM_C_SVC)
 
     def train(self, samples, responses):
-        self.model = cv2.SVM()
-        self.model.train(samples, responses, params = self.params)
+        self.model = cv2.ml.SVM_create()
+        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        return self.model.predict_all(samples).ravel()
+        return self.model.predict(samples)[1][0].ravel()
 
 
 def evaluate_model(model, digits, samples, labels):