diff --git a/CHANGELOG.md b/CHANGELOG.md
index c65f2758a6..1f8bbc6f76 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -52,6 +52,7 @@
 - PR #3117: Fix two crashes in experimental RF backend
 - PR #3119: Fix memset args for benchmark 
 - PR #3130: Return Python string from `dump_as_json()` of RF
+- PR #3136: Fix stochastic gradient descent example
 
 # cuML 0.16.0 (Date TBD)
 
diff --git a/python/cuml/solvers/sgd.pyx b/python/cuml/solvers/sgd.pyx
index 091fada5ed..8c968b2d1b 100644
--- a/python/cuml/solvers/sgd.pyx
+++ b/python/cuml/solvers/sgd.pyx
@@ -137,15 +137,15 @@ class SGD(Base):
         import cudf
         from cuml.solvers import SGD as cumlSGD
         X = cudf.DataFrame()
-        X['col1'] = np.array([1,1,2,2], dtype = np.float32)
-        X['col2'] = np.array([1,2,2,3], dtype = np.float32)
+        X['col1'] = np.array([1,1,2,2], dtype=np.float32)
+        X['col2'] = np.array([1,2,2,3], dtype=np.float32)
         y = cudf.Series(np.array([1, 1, 2, 2], dtype=np.float32))
         pred_data = cudf.DataFrame()
-        pred_data['col1'] = np.asarray([3, 2], dtype=dtype)
-        pred_data['col2'] = np.asarray([5, 5], dtype=dtype)
-        cu_sgd = cumlSGD(learning_rate=lrate, eta0=0.005, epochs=2000,
+        pred_data['col1'] = np.asarray([3, 2], dtype=np.float32)
+        pred_data['col2'] = np.asarray([5, 5], dtype=np.float32)
+        cu_sgd = cumlSGD(learning_rate='constant', eta0=0.005, epochs=2000,
                         fit_intercept=True, batch_size=2,
-                        tol=0.0, penalty=penalty, loss=loss)
+                        tol=0.0, penalty='none', loss='squared_loss')
         cu_sgd.fit(X, y)
         cu_pred = cu_sgd.predict(pred_data).to_array()
         print(" cuML intercept : ", cu_sgd.intercept_)
@@ -156,11 +156,11 @@ class SGD(Base):
 
     .. code-block:: python
 
-        cuML intercept :  0.004561662673950195
-        cuML coef :  0      0.9834546
-                    1    0.010128272
-                   dtype: float32
-        cuML predictions :  [3.0055666 2.0221121]
+        cuML intercept :  0.0041877031326293945
+        cuML coef :  0      0.984174
+                     1      0.009776
+                    dtype: float32
+        cuML predictions :  [3.005588  2.0214138]
 
 
     Parameters