diff --git a/docs/clearml_data/clearml_data_sdk.md b/docs/clearml_data/clearml_data_sdk.md
index 9e5ce96e..bcd93724 100644
--- a/docs/clearml_data/clearml_data_sdk.md
+++ b/docs/clearml_data/clearml_data_sdk.md
@@ -260,7 +260,7 @@ Dataset files must be uploaded before a dataset is [finalized](#finalizing-a-dat
 
 ## Finalizing a Dataset
 
-Use the [`Dataset.finalize`](../references/sdk/dataset.md#finalize) method to close the current dataset. This marks the 
+Use [`Dataset.finalize()`](../references/sdk/dataset.md#finalize) to close the current dataset. This marks the 
 dataset task as *Completed*, at which point, the dataset can no longer be modified. 
 
 Before closing a dataset, its files must first be [uploaded](#uploading-files).
@@ -268,7 +268,7 @@ Before closing a dataset, its files must first be [uploaded](#uploading-files).
 
 ## Syncing Local Storage
 
-Use the [`Dataset.sync_folder`](../references/sdk/dataset.md#sync_folder) method in order to update a dataset according
+Use [`Dataset.sync_folder()`](../references/sdk/dataset.md#sync_folder) in order to update a dataset according
 to a specific folder's content changes. Specify the folder to sync with the `local_path` parameter (the method assumes all files within the folder and recursive). 
 
 This method is useful in the case where there's a single point of truth, either a local or network folder, that gets updated periodically. 
@@ -276,7 +276,7 @@ The folder changes will be reflected in a new dataset version. This method saves
 update (add / remove) files in a dataset.
 
 ## Deleting Datasets
-Delete a dataset using the [`Dataset.delete`](../references/sdk/dataset.md#datasetdelete) class method. Input any of the 
+Delete a dataset using [`Dataset.delete()`](../references/sdk/dataset.md#datasetdelete) method. Input any of the 
 attributes of the dataset(s) you want to delete, including ID, project name, version, and/or dataset name. Multiple 
 datasets matching the query will raise an exception, unless you pass `entire_dataset=True` and `force=True`. In this 
 case, all matching datasets will be deleted. 
@@ -360,11 +360,11 @@ Note that in offline mode, any methods that require communicating with the serve
 `finalize()`, `get_local_copy()`, `get()`, `move_to_project()`, etc.). 
 
 Upload the offline dataset to the ClearML Server using [`Dataset.import_offline_session()`](../references/sdk/dataset.md#datasetimport_offline_session). 
+In the `session_folder_zip` argument, insert the path to the zip folder containing the dataset. To [upload](#uploading-files) 
+the dataset's data to network storage, set `upload` to `True`.  To [finalize](#finalizing-a-dataset) the dataset, 
+which will close it and prevent further modifications to the dataset, set `finalize` to `True`. 
 
 ```python
 Dataset.import_offline_session(session_folder_zip="<path_to_offline_dataset>", upload=True, finalize=True)
 ```
 
-In the `session_folder_zip` argument, insert the path to the zip folder containing the dataset. To [upload](#uploading-files) 
-the dataset's data to network storage, set `upload` to `True`.  To [finalize](#finalizing-a-dataset) the dataset, 
-which will close it and prevent further modifications to the dataset, set `finalize` to `True`. 
diff --git a/docs/clearml_data/data_management_examples/data_man_cifar_classification.md b/docs/clearml_data/data_management_examples/data_man_cifar_classification.md
index a5a7e7d0..47928932 100644
--- a/docs/clearml_data/data_management_examples/data_man_cifar_classification.md
+++ b/docs/clearml_data/data_management_examples/data_man_cifar_classification.md
@@ -43,7 +43,7 @@ New dataset created id=ee1c35f60f384e65bc800f42f0aca5ec
 Where `ee1c35f60f384e65bc800f42f0aca5ec` is the dataset ID.
 
 ## Adding Files
-Add the files that were just downloaded to the dataset: 
+Add the [downloaded files](#downloading-the-data) to the dataset: 
 
 ```
 clearml-data add --files <dataset_path>
diff --git a/docs/guides/advanced/multiple_tasks_single_process.md b/docs/guides/advanced/multiple_tasks_single_process.md
index e28b8706..74e9aeed 100644
--- a/docs/guides/advanced/multiple_tasks_single_process.md
+++ b/docs/guides/advanced/multiple_tasks_single_process.md
@@ -5,9 +5,9 @@ title: Multiple Tasks in Single Process
 The [multiple_tasks_single_process](https://github.com/allegroai/clearml/blob/master/examples/advanced/multiple_tasks_single_process.py)
 script demonstrates the capability to log a single script in multiple ClearML tasks.  
 
-In order to log a script in multiple tasks, each task needs to be initialized using the [`Task.init`](../../references/sdk/task.md#taskinit) 
-method with the `task_name` and `project_name` parameters input. Before initializing an additional task in the same script, the 
-previous task must be manually shut down with the [`close`](../../references/sdk/task.md#close) method. 
+In order to log a script in multiple tasks, each task needs to be initialized using [`Task.init()`](../../references/sdk/task.md#taskinit) 
+with the `task_name` and `project_name` parameters input. Before initializing an additional task in the same script, the 
+previous task must be manually shut down with [`Task.close()`](../../references/sdk/task.md#close). 
 
 When the script is executed, the console should display the following output:
 
diff --git a/docs/guides/pipeline/pipeline_controller.md b/docs/guides/pipeline/pipeline_controller.md
index 264c292e..5fadad6b 100644
--- a/docs/guides/pipeline/pipeline_controller.md
+++ b/docs/guides/pipeline/pipeline_controller.md
@@ -30,7 +30,7 @@ The sections below describe in more detail what happens in the controller task a
 
 ## The Pipeline Controller
 
-1. Create the [pipeline controller](../../references/sdk/automation_controller_pipelinecontroller.md) object.
+1. Create the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) object:
 
    ```python
    pipe = PipelineController(
@@ -90,7 +90,7 @@ The sections below describe in more detail what happens in the controller task a
    The [third step](#step-3---training-the-network) uses the pre-existing task `pipeline step 3 train model` in the 
    `examples` projects. The step uses Step 2's artifacts.
    
-1. Run the pipeline.
+1. Run the pipeline:
    
    ```python
    pipe.start()
@@ -103,7 +103,7 @@ The sections below describe in more detail what happens in the controller task a
 The pipeline's first step ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py))
 does the following: 
 
-1. Download data using [`StorageManager.get_local_copy`](../../references/sdk/storage.md#storagemanagerget_local_copy) 
+1. Download data using [`StorageManager.get_local_copy()`](../../references/sdk/storage.md#storagemanagerget_local_copy): 
   
    ```python
    # simulate local dataset, download one, so we have something local
@@ -111,7 +111,7 @@ does the following:
        remote_url='https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl'
    )
    ```    
-1. Store the data as an artifact named `dataset` using [`Task.upload_artifact`](../../references/sdk/task.md#upload_artifact)
+1. Store the data as an artifact named `dataset` using [`Task.upload_artifact()`](../../references/sdk/task.md#upload_artifact):
    ```python
    # add and upload local file containing our toy dataset
    task.upload_artifact('dataset', artifact_object=local_iris_pkl)
@@ -137,7 +137,7 @@ does the following:
    ```
 
 1. Download the data created in the previous step (specified through the `dataset_url` parameter) using 
-   [`StorageManager.get_local_copy`](../../references/sdk/storage.md#storagemanagerget_local_copy) 
+   [`StorageManager.get_local_copy`()](../../references/sdk/storage.md#storagemanagerget_local_copy) 
    
    ```python
    iris_pickle = StorageManager.get_local_copy(remote_url=args['dataset_url'])
@@ -167,13 +167,13 @@ does the following:
    task.connect(args)
    ```
    
-1. Clone the base task and enqueue it using [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely).
+1. Clone the base task and enqueue it using [`Task.execute_remotely()`](../../references/sdk/task.md#execute_remotely):
    
    ```python
    task.execute_remotely() 
    ```
    
-1. Access the data created in the previous task.
+1. Access the data created in the previous task:
    
    ```python
    dataset_task = Task.get_task(task_id=args['dataset_task_id'])
@@ -189,14 +189,14 @@ does the following:
 
 **To run the pipeline:**
 
-1. If the pipeline steps tasks do not yet exist, run their code to create the ClearML tasks.
+1. If the pipeline steps tasks do not yet exist, run their code to create the ClearML tasks:
    ```bash
    python step1_dataset_artifact.py
    python step2_data_processing.py
    python step3_train_model.py
    ``` 
    
-1. Run the pipeline controller.
+1. Run the pipeline controller:
    
    ```bash
    python pipeline_from_tasks.py
diff --git a/docs/guides/pipeline/pipeline_functions.md b/docs/guides/pipeline/pipeline_functions.md
index 8fb5cd89..72ebbba7 100644
--- a/docs/guides/pipeline/pipeline_functions.md
+++ b/docs/guides/pipeline/pipeline_functions.md
@@ -23,7 +23,7 @@ logged as required packages for the pipeline execution step.
 
 ## Pipeline Controller
 
-1. Create the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) object.
+1. Create the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) object:
 
    ```python
    pipe = PipelineController(
@@ -98,7 +98,7 @@ logged as required packages for the pipeline execution step.
     )
    ```
  
-1. Run the pipeline. 
+1. Run the pipeline: 
    ```python
    pipe.start()
    ```
diff --git a/docs/guides/reporting/using_artifacts.md b/docs/guides/reporting/using_artifacts.md
index f509f514..8a27e046 100644
--- a/docs/guides/reporting/using_artifacts.md
+++ b/docs/guides/reporting/using_artifacts.md
@@ -11,14 +11,14 @@ artifact and utilizes it.
 
 ## Task 1: Uploading an Artifact 
 
-The first task uploads a data file as an artifact using the [`Task.upload_artifact`](../../references/sdk/task.md#upload_artifact) 
-method, inputting the artifact's name and the location of the file.
+The first task uploads a data file as an artifact using [`Task.upload_artifact()`](../../references/sdk/task.md#upload_artifact), 
+and inputting the artifact's name and the location of the file.
 
 ```python
 task1.upload_artifact(name='data file', artifact_object='data_samples/sample.json')
 ```
 
-The task is then closed, using the [`Task.close`](../../references/sdk/task.md#close) method, so another task can be 
+The task is then closed, using [`Task.close()`](../../references/sdk/task.md#close), so another task can be 
 initialized in the same script. 
 
 Artifact details (location and size) can be viewed in ClearML's **web UI > experiment details > ARTIFACTS tab > OTHER section**.