diff --git a/doc/jvm/java_intro.rst b/doc/jvm/java_intro.rst
index c36b489ab..57a5866f9 100644
--- a/doc/jvm/java_intro.rst
+++ b/doc/jvm/java_intro.rst
@@ -6,15 +6,15 @@ This tutorial introduces Java API for XGBoost.
 **************
 Data Interface
 **************
-Like the XGBoost python module, XGBoost4J uses DMatrix to handle data,
-LIBSVM txt format file, sparse matrix in CSR/CSC format, and dense matrix is
+Like the XGBoost python module, XGBoost4J uses DMatrix to handle data.
+LIBSVM txt format file, sparse matrix in CSR/CSC format, and dense matrix are
 supported.
 
 * The first step is to import DMatrix:
 
   .. code-block:: java
 
-    import org.dmlc.xgboost4j.java.DMatrix;
+    import ml.dmlc.xgboost4j.java.DMatrix;
 
 * Use DMatrix constructor to load data from a libsvm text format file:
 
@@ -39,7 +39,8 @@ supported.
     long[] rowHeaders = new long[] {0,2,4,7};
     float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f};
     int[] colIndex = new int[] {0,2,0,3,0,1,2};
-    DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR);
+    int numColumn = 4;
+    DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR, numColumn);
   
   ... or in `Compressed Sparse Column (CSC) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS)>`_ format:
   
@@ -48,7 +49,8 @@ supported.
     long[] colHeaders = new long[] {0,3,4,6,7};
     float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f};
     int[] rowIndex = new int[] {0,1,2,2,0,2,1};
-    DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC);
+    int numRow = 3;
+    DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC, numRow);
 
 * You may also load your data from a dense matrix. Let's assume we have a matrix of form
 
@@ -66,7 +68,7 @@ supported.
     int nrow = 3;
     int ncol = 2;
     float missing = 0.0f;
-    DMatrix dmat = new Matrix(data, nrow, ncol, missing);
+    DMatrix dmat = new DMatrix(data, nrow, ncol, missing);
 
 * To set weight:
 
@@ -82,7 +84,7 @@ To set parameters, parameters are specified as a Map:
 
 .. code-block:: java
 
-  Map<String, Object> params = new HashMap<>() {
+  Map<String, Object> params = new HashMap<String, Object>() {
     {
       put("eta", 1.0);
       put("max_depth", 2);
@@ -101,8 +103,8 @@ With parameters and data, you are able to train a booster model.
 
   .. code-block:: java
 
-    import org.dmlc.xgboost4j.java.Booster;
-    import org.dmlc.xgboost4j.java.XGBoost;
+    import ml.dmlc.xgboost4j.java.Booster;
+    import ml.dmlc.xgboost4j.java.XGBoost;
 
 * Training
 
@@ -110,11 +112,13 @@ With parameters and data, you are able to train a booster model.
 
     DMatrix trainMat = new DMatrix("train.svm.txt");
     DMatrix validMat = new DMatrix("valid.svm.txt");
-    // Specify a watchList to see the performance
-    // Any Iterable<Entry<String, DMatrix>> object could be used as watchList
-    List<Entry<String, DMatrix>> watches = new ArrayList<>();
-    watches.add(new SimpleEntry<>("train", trainMat));
-    watches.add(new SimpleEntry<>("test", testMat));
+    // Specify a watch list to see model accuracy on data sets
+    Map<String, DMatrix> watches = new HashMap<String, DMatrix>() {
+      {
+        put("train", trainMat);
+        put("test", testMat);
+      }
+    };
     int nround = 2;
     Booster booster = XGBoost.train(trainMat, params, nround, watches, null, null);
 
@@ -130,15 +134,16 @@ With parameters and data, you are able to train a booster model.
 
   .. code-block:: java
 
-    String[] model_dump = booster.getModelDump(null, false)
+    // dump without feature map
+    String[] model_dump = booster.getModelDump(null, false);
     // dump with feature map
-    String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false)
+    String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false);
 
 * Load a model
 
   .. code-block:: java
 
-    Booster booster = Booster.loadModel("model.bin");
+    Booster booster = XGBoost.loadModel("model.bin");
 
 **********
 Prediction