diff --git a/README.md b/README.md
index c418f0e..abe46f3 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,14 @@
-Welcome to the sktime workshop at pydata global 2024
-====================================================
+# Welcome to the sktime workshop at PyData Global 2024
 
-This tutorial is about [skchange] and sktime [sktime].
+[![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=Workshop&color=red)](https://www.youtube.com/watch?v=VwhevNkxjYw)
 
-`skchange` is a python compatible framework library for detecting anomalies, changepoints in time series, and segmentation.
+This tutorial is about [skchange] and [sktime].
 
-`skchange` is based on, and extends, `sktime`, the most widely used scikit-learn compatible framework library for learning with time series.
+- `skchange` is a python compatible framework library for detecting anomalies, changepoints in time series, and segmentation.
+- `skchange` is based on, and extends, `sktime`, the most widely used scikit-learn compatible framework library for learning with time series.
 
 Both packages are maintained under permissive license, easily extensible by anyone, and interoperable with the python data science stack.
+
 This workshop gives a hands-on introduction to the new joint detection interface developed in skchange and sktime, for detecting point anomalies, changepoints, and segment anomalies.
 
 [skchange]: https://skchange.readthedocs.io/en/latest/
@@ -21,9 +22,9 @@ In the tutorial, we will move through notebooks section by section.
 
 You have different options how to run the tutorial notebooks:
 
-* Run the notebooks in the cloud on [Binder] - for this you don't have to install anything!
-* Run the notebooks on your machine. [Clone] this repository, get [conda], install the required packages (`sktime`, `seaborn`, `jupyter`) in an environment, and open the notebooks with that environment. For detail instructions, see below. For troubleshooting, see sktime's more detailed [installation instructions].
-* or, use python venv, and/or an editable install of this repo as a package. Instructions below.
+- Run the notebooks in the cloud on [Binder] - for this you don't have to install anything!
+- Run the notebooks on your machine. [Clone] this repository, get [conda], install the required packages (`sktime`, `seaborn`, `jupyter`) in an environment, and open the notebooks with that environment. For detail instructions, see below. For troubleshooting, see sktime's more detailed [installation instructions].
+- or, use python venv, and/or an editable install of this repo as a package. Instructions below.
 
 [Binder]: https://mybinder.org/v2/gh/sktime/sktime-tutorial-pydata-global-2024/main?filepath=notebooks
 [clone]: https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository
@@ -47,7 +48,6 @@ Both `skchange` and `sktime` are developed by open communities, with aims of eco
 
 We invite anyone to get involved as a developer, user, supporter (or any combination of these).
 
-
 ## :movie_camera: Other Tutorials
 
 - [EuroSciPy 2024 - Hierarchical, global forecasting, foundation models, extensions and marketplace](https://github.com/sktime/sktime-workshop-euroscipy2024)
@@ -70,7 +70,6 @@ We invite anyone to get involved as a developer, user, supporter (or any combina
 
 - [Pydata Global 2022 - Feature extraction, Pipelines, Tuning](https://github.com/sktime/sktime-tutorial-pydata-global-2022)
 
-
 ## :wave: How to contribute
 
 If you're interested in contributing to `skchange` or `sktime`,
@@ -82,34 +81,66 @@ Any contributions are welcome, not just code!
 
 To run the notebooks locally, you will need:
 
-* a local repository clone
-* a python environment with required packages installed
+- a local repository clone
+- a python environment with required packages installed
 
 ### Cloning the repository
 
 To clone the repository locally:
 
-`git clone https://github.com/sktime/sktime-tutorial-pydata-global-2024`
+```shell
+git clone https://github.com/sktime/sktime-tutorial-pydata-global-2024
+```
 
 ### Using conda env
 
 1. Create a python virtual environment:
-`conda create -y -n skchange_pydata python=3.11`
+
+    ```shell
+    conda create -y -n skchange_pydata python=3.11
+    ```
+
 2. Install required packages:
-`conda install -y -n skchange_pydata pip skchange sktime seaborn jupyter pmdarima statsmodels`
+
+    ```shell
+    conda install -y -n skchange_pydata pip skchange sktime seaborn jupyter pmdarima statsmodels
+    ```
+
 3. Activate your environment:
-`conda activate skchange_pydata`
+
+    ```shell
+    conda activate skchange_pydata
+    ```
+
 4. If using jupyter: make the environment available in jupyter:
-`python -m ipykernel install --user --name=skchange_pydata`
+
+    ```shell
+    python -m ipykernel install --user --name=skchange_pydata
+    ```
 
 ### Using python venv
 
 1. Create a python virtual environment:
-`python -m venv skchange_pydata`
+
+    ```shell
+    python -m venv skchange_pydata
+    ```
+
 2. Activate your environment:
- - `source skchange_pydata/bin/activate` for Linux
- - skchange_pydata/Scripts/activate` for Windows
+
+    ```shell
+    source skchange_pydata/bin/activate  # for Linux
+    skchange_pydata/Scripts/activate  # for Windows
+    ```
+
 3. Install the requirements:
-`pip install -r requirements`
+
+    ```shell
+    pip install -r requirements.txt
+    ```
+
 4. If using jupyter: make the environment available in jupyter:
-`python -m ipykernel install --user --name=skchange_pydata`
+
+    ```shell
+    python -m ipykernel install --user --name=skchange_pydata
+    ```