diff --git a/README.rst b/README.rst
index 20850964..ff770932 100644
--- a/README.rst
+++ b/README.rst
@@ -20,7 +20,8 @@ metric-learn contains efficient Python implementations of several popular superv
**Dependencies**
-- Python 3.6+
+- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was
+ `v0.5.0 `_)
- numpy, scipy, scikit-learn>=0.20.3
**Optional dependencies**
diff --git a/doc/getting_started.rst b/doc/getting_started.rst
index f1b35b4f..44fd1436 100644
--- a/doc/getting_started.rst
+++ b/doc/getting_started.rst
@@ -17,15 +17,16 @@ metric-learn can be installed in either of the following ways:
**Dependencies**
-- Python 2.7+, 3.4+
-- numpy, scipy, scikit-learn>=0.20.3
+- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was
+ `v0.5.0 `_)
+- numpy, scipy, scikit-learn>=0.20.3
**Optional dependencies**
- For SDML, using skggm will allow the algorithm to solve problematic cases
(install from commit `a0ed406 `_).
``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub.
-- For running the examples only: matplotlib
+- For running the examples only: matplotlib
Quick start
===========
diff --git a/doc/introduction.rst b/doc/introduction.rst
index 04ae1a18..7d9f52d0 100644
--- a/doc/introduction.rst
+++ b/doc/introduction.rst
@@ -96,7 +96,7 @@ examples (for code illustrating some of these use-cases, see the
metric learning provides a way to bias the clusters found by algorithms like
K-Means towards the intended semantics.
- Information retrieval: the learned metric can be used to retrieve the
- elements of a database that are semantically closer to a query element.
+ elements of a database that are semantically closest to a query element.
- Dimensionality reduction: metric learning may be seen as a way to reduce the
data dimension in a (weakly) supervised setting.
- More generally, the learned transformation :math:`L` can be used to project