diff --git a/C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_overfit.py b/C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_overfit.py
index 51d7e170..faa8cacf 100644
--- a/C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_overfit.py	
+++ b/C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_overfit.py	
@@ -333,7 +333,7 @@ def linear_regression(self):
         self.X_mapped_scaled, self.X_mu, self.X_sigma  = zscore_normalize_features(self.X_mapped)
 
         #linear_model = LinearRegression()
-        linear_model = Ridge(alpha=self.lambda_, normalize=True, max_iter=10000)
+        linear_model = Ridge(alpha=self.lambda_, max_iter=10000)
         linear_model.fit(self.X_mapped_scaled, self.y )
         self.w = linear_model.coef_.reshape(-1,)
         self.b = linear_model.intercept_
@@ -356,7 +356,7 @@ def logistic_regression(self):
         self.X_mapped, _ =  map_feature(self.X[:, 0], self.X[:, 1], self.degree)
         self.X_mapped_scaled, self.X_mu, self.X_sigma  = zscore_normalize_features(self.X_mapped)
         if not self.regularize or self.lambda_ == 0:
-            lr = LogisticRegression(penalty='none', max_iter=10000)
+            lr = LogisticRegression(penalty=None, max_iter=10000)
         else:
             C = 1/self.lambda_
             lr = LogisticRegression(C=C, max_iter=10000)