diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
index b47267db8ef5..6fbfa247c037 100644
--- a/docs/install/build_from_source.md
+++ b/docs/install/build_from_source.md
@@ -123,8 +123,7 @@ You can set the BLAS library explicitly by setting the BLAS variable to:
 
 See the [cmake/ChooseBLAS.cmake](https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake) file for the options.
 
-Intel's MKL (Math Kernel Library) is one of the most powerful math libraries
-https://software.intel.com/en-us/mkl
+[Intel's MKL (Math Kernel Library)](https://software.intel.com/en-us/mkl) is one of the most powerful math libraries
 
 It has following flavors:
 
@@ -144,6 +143,8 @@ shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN
 Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
 however it needs to be downloaded and installed manually before doing `cmake` configuration.
 Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).
+You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo)
+or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
 
 Note: MKL is supported only for desktop builds and the framework itself supports the following
 hardware:
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 460fa200cc46..516b2b3e796a 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -214,7 +214,7 @@ With MKL BLAS, the performace is expected to furtherly improved with variable ra
 You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
 Installing the full MKL installation enables MKL support for all operators under the linalg namespace.
 
-  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl)
+  1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl) You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo) or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.
 
   2. Run `make -j ${nproc} USE_BLAS=mkl`