diff --git a/MANIFEST.in b/MANIFEST.in
index e008f08fcfa5e5a464cd5d770e78987a4d3a6c46..12eef6d570fff423675c4c6e4b63ce5f8f262713 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,3 @@
-include LICENSE README.rst
+include LICENSE README.md
 recursive-include doc conf.py *.rst
 recursive-include src/bob/learn/em/data *.*
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2231428d28aae1958898e3679387d24bb0c04d54
--- /dev/null
+++ b/README.md
@@ -0,0 +1,48 @@
+
+[![badge doc](https://img.shields.io/badge/docs-latest-orange.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html)
+[![badge pipeline](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg)](https://gitlab.idiap.ch/bob/bob.learn.em/commits/master)
+[![badge coverage](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage)
+[![badge gitlab](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/bob/bob.learn.em)
+
+# Expectation Maximization Machine Learning Tools
+
+This package is part of the signal-processing and machine learning toolbox
+[Bob](https://www.idiap.ch/software/bob). It contains routines for learning
+probabilistic models via Expectation Maximization (EM).
+
+The EM algorithm is an iterative method that estimates parameters for
+statistical models, where the model depends on unobserved latent variables. The
+EM iteration alternates between performing an expectation (E) step, which
+creates a function for the expectation of the log-likelihood evaluated using
+the current estimate for the parameters, and a maximization (M) step, which
+computes parameters maximizing the expected log-likelihood found on the E step.
+These parameter-estimates are then used to determine the distribution of the
+latent variables in the next E step.
+
+The package includes the machine definition per se and a selection of different
+trainers for specialized purposes:
+
+ - Maximum Likelihood (ML)
+ - Maximum a Posteriori (MAP)
+ - K-Means
+ - Inter Session Variability Modelling (ISV)
+ - Joint Factor Analysis (JFA)
+ - Total Variability Modeling (iVectors)
+ - Probabilistic Linear Discriminant Analysis (PLDA)
+ - EM Principal Component Analysis (EM-PCA)
+
+
+## Installation
+
+Complete Bob's [installation](https://www.idiap.ch/software/bob/install)
+instructions. Then, to install this package, run:
+
+``` sh
+conda install bob.learn.em
+```
+
+
+## Contact
+
+For questions or reporting issues to this software package, contact our
+development [mailing list](https://www.idiap.ch/software/bob/discuss).
diff --git a/README.rst b/README.rst
deleted file mode 100644
index d64f771e2ad7ab1d8fee4a9a006de285a510bc64..0000000000000000000000000000000000000000
--- a/README.rst
+++ /dev/null
@@ -1,62 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-.. Mon 15 Aug 2016 09:48:28 CEST
-
-.. image:: https://img.shields.io/badge/docs-latest-orange.svg
-   :target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html
-.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg
-   :target: https://gitlab.idiap.ch/bob/bob.learn.em/commits/master
-.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg
-   :target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage
-.. image:: https://img.shields.io/badge/gitlab-project-0000c0.svg
-   :target: https://gitlab.idiap.ch/bob/bob.learn.em
-
-
-=================================================
- Expectation Maximization Machine Learning Tools
-=================================================
-
-This package is part of the signal-processing and machine learning toolbox
-Bob_. It contains routines for learning probabilistic models via Expectation
-Maximization (EM).
-
-The EM algorithm is an iterative method that estimates parameters for
-statistical models, where the model depends on unobserved latent variables. The
-EM iteration alternates between performing an expectation (E) step, which
-creates a function for the expectation of the log-likelihood evaluated using
-the current estimate for the parameters, and a maximization (M) step, which
-computes parameters maximizing the expected log-likelihood found on the E step.
-These parameter-estimates are then used to determine the distribution of the
-latent variables in the next E step.
-
-The package includes the machine definition per se and a selection of different trainers for specialized purposes:
-
- - Maximum Likelihood (ML)
- - Maximum a Posteriori (MAP)
- - K-Means
- - Inter Session Variability Modelling (ISV)
- - Joint Factor Analysis (JFA)
- - Total Variability Modeling (iVectors)
- - Probabilistic Linear Discriminant Analysis (PLDA)
- - EM Principal Component Analysis (EM-PCA)
-
-
-Installation
-------------
-
-Complete Bob's `installation`_ instructions. Then, to install this package,
-run::
-
-  $ conda install bob.learn.em
-
-
-Contact
--------
-
-For questions or reporting issues to this software package, contact our
-development `mailing list`_.
-
-
-.. Place your references here:
-.. _bob: https://www.idiap.ch/software/bob
-.. _installation: https://www.idiap.ch/software/bob/install
-.. _mailing list: https://www.idiap.ch/software/bob/discuss
diff --git a/pyproject.toml b/pyproject.toml
index a9a956d5d74fe5decd9dbfad345bc36760ff3a0f..5172740323c574fd2cee9f24e6237dfe9a547326 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,8 +58,7 @@
     package-dir = {"" = "src"}
 
 [tool.setuptools.dynamic]
-    readme = {file = "README.rst"}
-
+    readme = {file = "README.md", content-type = "text/markdown"}
 
 [tool.distutils.bdist_wheel]
     universal = true