[EN]-AI training first ML model

Some grammar, code, and consistency fixes.

Author: SR-OVH-US

pages/public_cloud/ai_machine_learning/training_tuto_01_train_your_first_model/guide.en-gb.md

@@ -21,7 +21,7 @@ At the end of this tutorial, you will have learned to master **OVHcloud AI Train
 We will show you how you can:
 
 - **Upload your data** to the OVHcloud Object Storage. 
-- **Launch your training job and attach your data to its environment**, so your model can access to your data.
+- **Launch your training job and attach your data to its environment**, so your model can access your data.
 - **Monitor** the progress of your job.
 - **Download your model** in the cloud in order to retrieve it, once trained.
 
@@ -66,9 +66,9 @@ Once your object container is created, you will see it in the Object Storage lis
 
 > [!primary]
 >
-> Using the manager to upload your data can be very long. We recommend to use the OVHcloud AI CLI.
+> Using the manager to upload your data can be very long. We recommend using the OVHcloud AI CLI.
 >
-> In the OVHcloud Control Panel, you can upload files but not folders. For instance, you can upload a `.zip` file to optimize the bandwidth, then unzip it in your code. But if your dataset is already split in several folders, you must use the AI CLI to upload them.
+> In the OVHcloud Control Panel, you can upload files but not folders. For instance, you can upload a `.zip` file to optimize the bandwidth, then unzip it in your code. But if your dataset is already split into several folders, you must use the AI CLI to upload them.
 >
 
 #### 1.2 - Upload your data via CLI
@@ -124,10 +124,10 @@ Then, in the `Enter the Docker command` step, you can specify the command that a
 
 - Example:
 
-Assuming you have added your `main.py` file and your `requirements.txt` file to a container that you have linked to your job with `my_data` as your mount directory, you can then use: 
+Assuming you have added your `train-first-model.py` file and your `requirements.txt` file to a container that you have linked to your job with `/workspace/my_data` as your mount directory, you can then use: 
 
 ```console
--- bash -c 'pip install -r /workspace/my_data/requirements.txt && python /workspace/my_data/cnn_classification_mode_dataset.py'
+-- bash -c 'pip install -r /workspace/my_data/requirements.txt && python /workspace/my_data/train-first-model.py'
 ```
 
 > [!primary]
@@ -172,12 +172,12 @@ Depending on your needs, you can select the `permission_mode` you want (Read-Onl
 
 Otherwise, you can remove the --volume line, since it will not bring anything to your app.
 
-To give you a real example, here is the command we will use to launch our job, assuming this time that our `dataset.zip` is contained in a `fashion_MNIST_dataset` container, with a `mount_directory` named `my_data`, and that our Python file and our `requirements.txt` file are in the `ovh/ai-training-examples` GitHub repository: 
+To give you a real example, here is the command we will use to launch our job, assuming this time that our `my-dataset.zip` is contained in a `fashion_MNIST_dataset` container, with a `mount_directory` named `/workspace/my_data`, and that our Python file and our `requirements.txt` file are in the `ovh/ai-training-examples` GitHub repository: 
 
 ```console
 ovhai job run ovhcom/ai-training-pytorch \
 	  --gpu 1 \
-	  --volume fashion_to_delete@GRA/:/workspace/my_data:RW \
+	  --volume fashion_MNIST_dataset@GRA/:/workspace/my_data:RW \
 	  --volume https://github.com/ovh/ai-training-examples.git:/workspace/github_repo:RO \
 	  -- bash -c 'pip install -r /workspace/github_repo/jobs/getting-started/train-first-model/requirements.txt && python /workspace/github_repo/jobs/getting-started/train-first-model/train-first-model.py'
 ```