[DOC] Fix typos in getting started section of the documentation (#1399)

### Description

Fixes some typos in the getting started section of the documentation.

There is also a broken link (line 54, favourite package). After a brief
look at the documentation of pytorch lightning, I am not really sure
with that should be replaced. So feel free to add a commit with the
corrected link. Otherwise I could also open an issue for that. Just let
me know :)

### Checklist

- [ ] Linked issues (if existing)
- [ ] Amended changelog for large changes (and added myself there as
contributor)
- [ ] Added/modified tests
- [ ] Used pre-commit hooks when committing to ensure that code is
compliant with hooks. Install hooks with `pre-commit install`.
To run hooks independent of commit, execute `pre-commit run --all-files`

Make sure to have fun coding!

Co-authored-by: Felix Hirwa Nshuti <hirwanshutiflx@gmail.com>

Author: pietsjoh

docs/source/getting-started.rst

@@ -23,12 +23,11 @@ Otherwise, you can proceed with
 
 
 Alternatively, to install the package via ``conda``:
-
 .. code-block:: bash
 
     conda install pytorch-forecasting pytorch>=1.7 -c pytorch -c conda-forge
 
-PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is install from the pytorch channel.
+PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is installed from the pytorch channel.
 
 To use the MQF2 loss (multivariate quantile loss), also install
 
@@ -54,7 +53,7 @@ The general setup for training and testing a model is
    Similarly, a test dataset or later a dataset for inference can be created. You can store the dataset parameters
    directly if you do not wish to load the entire training dataset at inference time.
 
-#. Instantiate a model using the its ``.from_dataset()`` method.
+#. Instantiate a model using the ``.from_dataset()`` method.
 #. Create a ``lightning.Trainer()`` object.
 #. Find the optimal learning rate with its ``.tuner.lr_find()`` method.
 #. Train the model with early stopping on the training dataset and use the tensorboard logs
@@ -65,7 +64,7 @@ The general setup for training and testing a model is
 #. Load the model from the model checkpoint and apply it to new data.
 
 
-The :ref:`Tutorials <tutorials>` section provides detailled guidance and examples on how to use models and implement new ones.
+The :ref:`Tutorials <tutorials>` section provides detailed guidance and examples on how to use models and implement new ones.
 
 
 Example