Fix documentation

CONTRIBUTING.md

@@ -7,7 +7,7 @@ Frouros is an open-source project. Anyone with an interest in the project can jo
 Recommended steps for first time contributors:
 
 1. Fork repository on GitHub.
-2. Set up develop environment (it is not mandatory, but we highly recommend the use of a [virtual environment](https://docs.python.org/3.9/library/venv.html)):
+2. Set up develop environment (it is not mandatory, but we highly recommend the use of a [virtual environment](https://docs.python.org/3.11/library/venv.html)):
     ```bash
     python3 -m venv .venv
     source .venv/bin/activate

docs/source/concepts.md

@@ -49,13 +49,13 @@ Drift detection methods can be classified according to the type of drift they ca
 
 Their main objective is to **detect concept drift**. They are closely related to data stream mining, online and incremental learning. 
 
-At the time of writing this, Frouros only implements *concept drift* detectors that work in a streaming manner. This means that the detector can only be updated with a single sample each time. 
+At the time of writing this, Frouros only implements *concept drift* detectors that work in a {doc}`streaming </api_reference/detectors/concept_drift/streaming>` manner. This means that the detector can only be updated with a single sample each time. 
 
 ### Data drift
 
 On the other hand, there are problems where it is very costly or even impossible to obtain labels in a reasonable amount of time (see [verification latency](#verification-latency-or-delay)). In this case, is not possible to directly check if *concept drift* is occurring, so **detect data drift** becomes the main objective of these type of methods.
 
-Al the time of writing this, Frouros implements detectors that are capable to work in {ref}`batch <batch>` or {ref}`streaming <streaming>` mode. In addition, we can difference between univariate and multivariate data drift detectors, according to the type of feature/covariate distributions used.
+At the time of writing this, Frouros implements detectors that are capable to work in {doc}`batch </api_reference/detectors/data_drift/batch>` or {doc}`streaming </api_reference/detectors/data_drift/streaming>` mode. In addition, we can difference between univariate and multivariate data drift detectors, according to the type of feature/covariate distributions used.
 ```{bibliography}
 :filter: docname in docnames
 ```