diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java
index b0115756cf45..3f034588c952 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java
@@ -23,12 +23,16 @@
 
 import org.apache.spark.ml.regression.AFTSurvivalRegression;
 import org.apache.spark.ml.regression.AFTSurvivalRegressionModel;
-import org.apache.spark.mllib.linalg.*;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
 import org.apache.spark.sql.SparkSession;
-import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
 // $example off$
 
 /**
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
index 5f964aca9209..a954dbd20c12 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
@@ -47,7 +47,7 @@ public static void main(String[] args) {
       RowFactory.create(2, 0.2)
     );
     StructType schema = new StructType(new StructField[]{
-      new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+      new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
       new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
     });
     Dataset<Row> continuousDataFrame = spark.createDataFrame(data, schema);
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java
index f8f2fb14be1f..fcf90d8d1874 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java
@@ -25,8 +25,8 @@
 import java.util.List;
 
 import org.apache.spark.ml.feature.ChiSqSelector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
 import org.apache.spark.sql.types.DataTypes;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java
index eee92c77a8c5..66ce23b49d36 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java
@@ -25,8 +25,8 @@
 import java.util.List;
 
 import org.apache.spark.ml.feature.DCT;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
 import org.apache.spark.sql.types.Metadata;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
index 889f5785dfd8..9e07a0c2f899 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
@@ -19,16 +19,20 @@
 
 // $example on$
 import java.util.Arrays;
-// $example off$
+import java.util.List;
 
-// $example on$
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
 // $example off$
 import org.apache.spark.sql.SparkSession;
 
@@ -44,15 +48,17 @@ public static void main(String[] args) {
 
     // $example on$
     // Prepare training data.
-    // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans into
-    // DataFrames, where it uses the bean metadata to infer the schema.
-    Dataset<Row> training = spark.createDataFrame(
-      Arrays.asList(
-        new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
-        new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
-        new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-        new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
-      ), LabeledPoint.class);
+    List<Row> dataTraining = Arrays.asList(
+        RowFactory.create(1.0, Vectors.dense(0.0, 1.1, 0.1)),
+        RowFactory.create(0.0, Vectors.dense(2.0, 1.0, -1.0)),
+        RowFactory.create(0.0, Vectors.dense(2.0, 1.3, 1.0)),
+        RowFactory.create(1.0, Vectors.dense(0.0, 1.2, -0.5))
+    );
+    StructType schema = new StructType(new StructField[]{
+        new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+        new StructField("features", new VectorUDT(), false, Metadata.empty())
+    });
+    Dataset<Row> training = spark.createDataFrame(dataTraining, schema);
 
     // Create a LogisticRegression instance. This instance is an Estimator.
     LogisticRegression lr = new LogisticRegression();
@@ -87,11 +93,12 @@ public static void main(String[] args) {
     System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
     // Prepare test documents.
-    Dataset<Row> test = spark.createDataFrame(Arrays.asList(
-      new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-      new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-      new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
-    ), LabeledPoint.class);
+    List<Row> dataTest = Arrays.asList(
+        RowFactory.create(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+        RowFactory.create(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+        RowFactory.create(1.0, Vectors.dense(0.0, 2.2, -1.5))
+    );
+    Dataset<Row> test = spark.createDataFrame(dataTest, schema);
 
     // Make predictions on test documents using the Transformer.transform() method.
     // LogisticRegression.transform will only use the 'features' column.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
index dcd209e28e2b..a561b6d39ba8 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
@@ -21,7 +21,7 @@
 import org.apache.spark.ml.regression.LinearRegression;
 import org.apache.spark.ml.regression.LinearRegressionModel;
 import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java
index 5d29e5454921..a15e5f84a187 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java
@@ -53,7 +53,7 @@ public static void main(String[] args) {
     );
 
     StructType schema = new StructType(new StructField[]{
-      new StructField("id", DataTypes.DoubleType, false, Metadata.empty()),
+      new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
       new StructField("category", DataTypes.StringType, false, Metadata.empty())
     });
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java
index ffa979ee013a..d597a9a2ed0b 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java
@@ -25,8 +25,8 @@
 
 import org.apache.spark.ml.feature.PCA;
 import org.apache.spark.ml.feature.PCAModel;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
index 7afcd0e50cd9..67180df65c72 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
@@ -24,8 +24,8 @@
 import java.util.List;
 
 import org.apache.spark.ml.feature.PolynomialExpansion;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
index 6e0753959efd..800e42c949cb 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
@@ -25,7 +25,7 @@
 import org.apache.spark.ml.feature.IDF;
 import org.apache.spark.ml.feature.IDFModel;
 import org.apache.spark.ml.feature.Tokenizer;
-import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
@@ -45,9 +45,9 @@ public static void main(String[] args) {
 
     // $example on$
     List<Row> data = Arrays.asList(
-      RowFactory.create(0, "Hi I heard about Spark"),
-      RowFactory.create(0, "I wish Java could use case classes"),
-      RowFactory.create(1, "Logistic regression models are neat")
+      RowFactory.create(0.0, "Hi I heard about Spark"),
+      RowFactory.create(0.0, "I wish Java could use case classes"),
+      RowFactory.create(1.0, "Logistic regression models are neat")
     );
     StructType schema = new StructType(new StructField[]{
       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java
index 41f1d8750ac4..9bb0f93d3a6a 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java
@@ -23,8 +23,8 @@
 import java.util.Arrays;
 
 import org.apache.spark.ml.feature.VectorAssembler;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java
index 24959c0e10f2..19b8bc83be6e 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java
@@ -28,7 +28,7 @@
 import org.apache.spark.ml.attribute.AttributeGroup;
 import org.apache.spark.ml.attribute.NumericAttribute;
 import org.apache.spark.ml.feature.VectorSlicer;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;