Update README.md

README.md

@@ -27,11 +27,18 @@ For details on MOMENT models, training data, and experimental results, please re
 
 # Usage
 
-Install the package using:
+**Recommended Python Version:** Python 3.11 (support for additional versions is expected soon).
+
+You can install the `momentfm` package using pip:
+```bash
+pip install momentfm
+```
+Alternatively, to install the latest version directly from the GitHub repository:
 ```bash
 pip install git+https://github.com/moment-timeseries-foundation-model/moment.git
 ```
 
+
 To load the pre-trained model for one of the tasks, use one of the following code snippets:
 
 **Forecasting**
@@ -84,6 +91,15 @@ model = MOMENTPipeline.from_pretrained(
 )
 ```
 
+### Tutorials
+Here is the list of tutorials and reproducibile experiments to get started with MOMENT for various tasks:
+- [Forecasting](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/forecasting.ipynb)
+- [Classification](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/classification.ipynb)
+- [Anomaly Detection](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/anomaly_detection.ipynb)
+- [Imputation](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/imputation.ipynb)
+- [Representation Learning](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/representation_learning.ipynb)
+- [Real-world Electrocardiogram (ECG) Case Study](https://github.com/moment-timeseries-foundation-model/moment/blob/main/tutorials/ptbxl_classification.ipynb) -- This tutorial also shows how to fine-tune MOMENT for a real-world ECG classification problem, performing training and inference on multiple GPUs and parameter efficient fine-tuning (PEFT). 
+
 ## Model Details
 
 ### Model Description