Small edits (#738)

docs/apps/clearml_task.md

@@ -19,7 +19,7 @@ line arguments, Python module dependencies, and a requirements.txt file!
 
 1. Execute `clearml-task`, specifying the ClearML target project and task name, along with your script (and repository / commit / branch). 
    Optionally, specify an execution queue and Docker image to use.
-1. `clearml-task` does its magic! It creates a new task on the [ClearML Server](../deploying_clearml/clearml_server.md), 
+1. `clearml-task` does its magic! It creates a new [ClearML Task](../fundamentals/task.md), 
    and, if so directed, enqueues it for execution by a ClearML Agent.
 1. While the Task is running on the remote machine, all its console outputs are logged in real-time, alongside your 
    TensorBoard and matplotlib. You can track your script's progress and results in the [ClearML Web UI](../webapp/webapp_overview.md) 

docs/clearml_data/data_management_examples/data_man_python.md

@@ -18,21 +18,24 @@ demonstrates how to do the following:
 
 ### Downloading the Data
 
-You first need to obtain a local copy of the CIFAR dataset.
+You first need to obtain a local copy of the CIFAR dataset. 
+The code below downloads the data and `dataset_path` contains the path to the downloaded data: 
 
- ```python
- from clearml import StorageManager
+```python
+from clearml import StorageManager
 
- manager = StorageManager()
- dataset_path = manager.get_local_copy(
-     remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
- )
+manager = StorageManager()
+dataset_path = manager.get_local_copy(
+    remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+)
 ```
 
-This script downloads the data and `dataset_path` contains the path to the downloaded data. 
 
 ### Creating the Dataset
 
+The following code creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
+can be viewed in the [WebApp](../../webapp/datasets/webapp_dataset_viewing.md).
+
 ```python
 from clearml import Dataset
 
@@ -42,23 +45,24 @@ dataset = Dataset.create(
 )
  ```
 
-This creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
-can be viewed in the WebApp.
 
 ### Adding Files
 
+Add the downloaded files to the current dataset:
+
 ```python
 dataset.add_files(path=dataset_path)
 ```
 
-This adds the downloaded files to the current dataset.  
-
 ### Uploading the Files
 
+Upload the dataset: 
+
 ```python
 dataset.upload()
 ```
-This uploads the dataset to the ClearML Server by default. The dataset's destination can be changed by specifying the 
+
+By default, the dataset is uploaded to the ClearML File Server. The dataset's destination can be changed by specifying the 
 target storage with the `output_url` parameter of the [`upload`](../../references/sdk/dataset.md#upload) method. 
 
 ### Finalizing the Dataset

docs/guides/clearml-task/clearml_task_tutorial.md

@@ -7,9 +7,8 @@ on a remote or local machine, from a remote repository and your local machine.
 
 ### Prerequisites
 
-- `clearml` Python package installed 
-
-- `clearml-agent` running on at least one machine (to execute the experiment), configured to listen to default queue 
+- [`clearml`](../../getting_started/ds/ds_first_steps.md) Python package installed and configured
+- [`clearml-agent`](../../clearml_agent.md#installation) running on at least one machine (to execute the experiment), configured to listen to `default` queue 
 
 ### Executing Code from a Remote Repository 
 
@@ -34,9 +33,9 @@ or add the `--packages "<package_name>"` option to the command (for example: `--
 ::: 
 
 Now `clearml-task` does all the heavy-lifting!
-1. It creates a new Task on the [ClearML Server](../../deploying_clearml/clearml_server.md).
+1. It creates a new [ClearML Task](../../fundamentals/task.md) 
 1. `clearml-task` enqueues the task in the selected execution queue, where a [ClearML Agent](../../clearml_agent.md) 
-   assigned to that queue executes the task.  
+   assigned to that queue executes the task  
      
 Your output should look something like this: