Small edits (#987)

docs/getting_started/ds/best_practices.md

@@ -21,7 +21,7 @@ During early stages of model development, while code is still being modified hea
   - **Workstation with a GPU**, usually with a limited amount of memory for small batch-sizes. Use this workstation to train 
     the model and ensure that you choose a model that makes sense, and the training procedure works. Can be used to provide initial models for testing. 
 
-The abovementioned setups might be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome! 
+These setups can be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome! 
 The goal of this phase is to get a code, dataset, and environment set up, so you can start digging to find the best model!
 
 - [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [Getting Started](ds_first_steps.md)). 

docs/getting_started/ds/ds_first_steps.md

@@ -6,7 +6,7 @@ title: First Steps
 ## Install ClearML
 
 
-First, [sign up for free](https://app.clear.ml)
+First, [sign up for free](https://app.clear.ml).
 
 Install the `clearml` python package:
 ```bash

docs/getting_started/video_tutorials/experiment_manager_hands-on.md

@@ -46,7 +46,7 @@ We can change the task’s name by clicking it here, and add a description or ge
 
 First of all, source code is captured. If you’re working in a git repository we’ll save your git information along with any uncommitted changes. If you’re running an unversioned script, `clearml` will save the script instead.
 
-Together with the python packages your coded uses, this’ll allow you to recreate your experiment on any machine.
+Together with the Python packages your code uses, this will allow you to recreate your experiment on any machine.
 
 Similarly, all of the output the code produces will also be captured.