diff --git a/docs/clearml_data/clearml_data_sdk.md b/docs/clearml_data/clearml_data_sdk.md
index c14e0794..d06318a6 100644
--- a/docs/clearml_data/clearml_data_sdk.md
+++ b/docs/clearml_data/clearml_data_sdk.md
@@ -28,8 +28,8 @@ ClearML Data supports multiple ways to create datasets programmatically, which p
   will inherit its data
 * [`Dataset.squash()`](#datasetsquash) - Generate a new dataset from by squashing together a set of related datasets
 
-You can add metadata to your datasets using the `Dataset.set_metadata` method, and access the metadata using the
-`Dataset.get_metadata` method. See [`set_metadata`](../references/sdk/dataset.md#set_metadata) and [`get_metadata`](../references/sdk/dataset.md#get_metadata).
+You can add metadata to your datasets using [`Dataset.set_metadata()`](../references/sdk/dataset.md#set_metadata), 
+and access the metadata using [`Dataset.get_metadata()`](../references/sdk/dataset.md#get_metadata).
 
 ### Dataset.create()
 
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 88ed6a00..a5a7e7d0 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
@@ -102,7 +102,7 @@ hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset's ID
 `dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way 
 you can easily track which dataset the task is using. 
 
-The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method returns a path to the cached, 
+[`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy) returns a path to the cached, 
 downloaded dataset. Then the dataset path is input to PyTorch's `datasets` object.
 
 The script then trains a neural network to classify images using the dataset created above.
\ No newline at end of file
diff --git a/docs/clearml_serving/clearml_serving_tutorial.md b/docs/clearml_serving/clearml_serving_tutorial.md
index 2a03e75f..1b8a2303 100644
--- a/docs/clearml_serving/clearml_serving_tutorial.md
+++ b/docs/clearml_serving/clearml_serving_tutorial.md
@@ -241,8 +241,8 @@ You can also specify per-endpoint log frequency with the `clearml-serving` CLI.
 
 See examples of ClearML Serving with other supported frameworks:
 
-* [Scikit-Learn](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/readme.md) - random data
-* [Scikit-Learn Model Ensemble](https://github.com/allegroai/clearml-serving/blob/main/examples/ensemble/readme.md) - random data
+* [scikit-learn](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/readme.md) - random data
+* [scikit-learn Model Ensemble](https://github.com/allegroai/clearml-serving/blob/main/examples/ensemble/readme.md) - random data
 * [XGBoost](https://github.com/allegroai/clearml-serving/blob/main/examples/xgboost/readme.md) - iris dataset
 * [LightGBM](https://github.com/allegroai/clearml-serving/blob/main/examples/lightgbm/readme.md) - iris dataset
 * [PyTorch](https://github.com/allegroai/clearml-serving/blob/main/examples/pytorch/readme.md) - mnist dataset
diff --git a/docs/configs/clearml_conf.md b/docs/configs/clearml_conf.md
index 18c6e97c..4a0e6150 100644
--- a/docs/configs/clearml_conf.md
+++ b/docs/configs/clearml_conf.md
@@ -416,7 +416,7 @@ match_rules: [
           image: "nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04"
           arguments: "-e define=value"
           match: {
-              script{
+              script {
                   # Optional: must match all requirements (not partial)
                   requirements: {
                       # version selection matching PEP-440
diff --git a/docs/faq.md b/docs/faq.md
index f553349f..f64099e3 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -128,7 +128,7 @@ When a new ClearML Server version is available, the notification is:
 
 #### How do I find out ClearML version information?   <a id="versions"></a>
 
-ClearML server version information is available in the ClearML webapp Settings page. On the bottom right of the page, 
+ClearML server version information is available in the ClearML WebApp **Settings** page. On the bottom right of the page, 
 it says **Version**, followed by three numbers: the web application version, the API server version, and the API version.
 
 ![Server version information](img/faq_server_versions.png)
diff --git a/docs/getting_started/ds/ds_second_steps.md b/docs/getting_started/ds/ds_second_steps.md
index f21248b3..8ea9987b 100644
--- a/docs/getting_started/ds/ds_second_steps.md
+++ b/docs/getting_started/ds/ds_second_steps.md
@@ -115,7 +115,7 @@ under the "Input Models" section.
 Check out model snapshots examples for [TensorFlow](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
 [PyTorch](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py),
 [Keras](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py),
-[Scikit-Learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
+[scikit-learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
 
 #### Loading Models
 Loading a previously trained model is quite similar to loading artifacts.
diff --git a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md
index 104f4c31..9ce2bbfb 100644
--- a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md
+++ b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md
@@ -1,5 +1,5 @@
 ---
-title: Scikit-Learn with Joblib 
+title: scikit-learn with Joblib 
 ---
 
 The [sklearn_joblib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py) 
diff --git a/docs/guides/pipeline/pipeline_controller.md b/docs/guides/pipeline/pipeline_controller.md
index b16b653f..bf6901d1 100644
--- a/docs/guides/pipeline/pipeline_controller.md
+++ b/docs/guides/pipeline/pipeline_controller.md
@@ -50,7 +50,7 @@ The sections below describe in more detail what happens in the controller task a
 1. Build the pipeline (see [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
    method for complete reference):
 
-   The pipeline's [first step](#step-1---downloading-the-datae) uses the pre-existing task 
+   The pipeline's [first step](#step-1---downloading-the-data) uses the pre-existing task 
    `pipeline step 1 dataset artifact` in the `examples` project. The step uploads local data and stores it as an artifact.
 
    ```python
diff --git a/docs/guides/reporting/3d_plots_reporting.md b/docs/guides/reporting/3d_plots_reporting.md
index 1a1df0f4..06cd0da4 100644
--- a/docs/guides/reporting/3d_plots_reporting.md
+++ b/docs/guides/reporting/3d_plots_reporting.md
@@ -27,7 +27,7 @@ Logger.current_logger().report_surface(
     zaxis="title Z",
 )
 ```
-Visualize the reported surface plot in **PLOTS**.
+View the reported surface plot in **PLOTS**.
 
 ![Surface plot](../../img/examples_reporting_02.png)
 
@@ -49,5 +49,5 @@ Logger.current_logger().report_scatter3d(
 )
 ```
 
-Visualize the reported 3D scatter plot in **PLOTS**.
+View the reported 3D scatter plot in **PLOTS**.
 ![3d scatter plot](../../img/examples_reporting_01.png)
diff --git a/docs/guides/reporting/clearml_logging_example.md b/docs/guides/reporting/clearml_logging_example.md
index 6ec74a2d..237eaac2 100644
--- a/docs/guides/reporting/clearml_logging_example.md
+++ b/docs/guides/reporting/clearml_logging_example.md
@@ -17,8 +17,8 @@ In the ``clearml`` GitHub repository, this example includes a clickable icon to
 
 ## Scalars
 
-To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar) 
-method. The scalar plots appear in the **web UI** in **SCALARS**.
+To reports scalars, call [Logger.report_scalar()](../../references/sdk/logger.md#report_scalar). 
+The scalar plots appear in the **web UI** in **SCALARS**.
 
 ```python
 # report two scalar series on two different graphs
@@ -44,7 +44,7 @@ Plots appear in **PLOTS**.
 
 ### 2D Plots
 
-Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) method. 
+Report 2D scatter plots by calling [Logger.report_scatter2d()](../../references/sdk/logger.md#report_scatter2d). 
 Use the `mode` parameter to plot data points as markers, or both lines and markers.
 
 ```python
@@ -67,7 +67,7 @@ logger.report_scatter2d(
 
 ### 3D Plots
 
-To plot a series as a 3D scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
+To plot a series as a 3D scatter plot, use [Logger.report_scatter3d()](../../references/sdk/logger.md#report_scatter3d).
 
 ```python
 # report 3d scatter plot
@@ -85,8 +85,7 @@ logger.report_scatter3d(
 
 ![3d scatter plot](../../img/colab_explicit_reporting_05.png)
 
-To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface) 
-method.
+To plot a series as a surface plot, use [Logger.report_surface()](../../references/sdk/logger.md#report_surface).
 
 ```python
 # report 3d surface
diff --git a/docs/guides/reporting/model_config.md b/docs/guides/reporting/model_config.md
index bbb9f2b7..7f21d696 100644
--- a/docs/guides/reporting/model_config.md
+++ b/docs/guides/reporting/model_config.md
@@ -25,8 +25,7 @@ output_model = OutputModel(task=task)
 
 ## Label Enumeration
 
-Set the model's label enumeration using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels) 
-method.
+Set the model's label enumeration using [`OutputModel.update_labels()`](../../references/sdk/model_outputmodel.md#update_labels).
 
 ```python
 labels = {"background": 0, "cat": 1, "dog": 2}
@@ -34,8 +33,8 @@ output_model.update_labels(labels)
 ```
 
 ## Registering Models
-Register a previously trained model using the [`OutputModel.update_weights`](../../references/sdk/model_outputmodel.md#update_weights) 
-method. The example code uses a model stored in S3.
+Register a previously trained model using [`OutputModel.update_weights()`](../../references/sdk/model_outputmodel.md#update_weights). 
+The example code uses a model stored in S3.
 
 ```python
 # Manually log a model file, which will have the labels connected above
diff --git a/docs/guides/ui/building_leader_board.md b/docs/guides/ui/building_leader_board.md
index d849b250..0186bf7d 100644
--- a/docs/guides/ui/building_leader_board.md
+++ b/docs/guides/ui/building_leader_board.md
@@ -51,7 +51,7 @@ The experiments table allows filtering experiments by experiment name, type, and
     * **Aborted** - The experiment ran and was manually or programmatically terminated.
     * **Published** - The experiment is not running, it is preserved as read-only.
 
-## Step 3: Hide the Defaults Column
+## Step 3: Hide the Default Columns
 
 Customize the columns on the tracking leaderboard by hiding any of the default columns shown below.
 
diff --git a/docs/integrations/scikit_learn.md b/docs/integrations/scikit_learn.md
index 5960f724..646f7d3e 100644
--- a/docs/integrations/scikit_learn.md
+++ b/docs/integrations/scikit_learn.md
@@ -1,5 +1,5 @@
 ---
-title: Scikit-Learn
+title: scikit-learn
 ---
 
 :::tip
@@ -7,7 +7,7 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d
 instructions.
 :::
 
-ClearML integrates seamlessly with [Scikit-Learn](https://scikit-learn.org/stable/), automatically logging models created
+ClearML integrates seamlessly with [scikit-learn](https://scikit-learn.org/stable/), automatically logging models created
 with `joblib`.
 
 All you have to do is simply add two lines of code to your scikit-learn script:
@@ -73,8 +73,8 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
 
 Take a look at ClearML's scikit-learn examples. The examples use scikit-learn and ClearML in different configurations with 
 additional tools, like Matplotlib: 
-* [Scikit-Learn with Joblib](../guides/frameworks/scikit-learn/sklearn_joblib_example.md) - Demonstrates ClearML automatically logging the models created with joblib and a scatter plot created by Matplotlib.
-* [Scikit-Learn with Matplotlib](../guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) - Demonstrates ClearML automatically logging scatter diagrams created with Matplotlib.
+* [scikit-learn with Joblib](../guides/frameworks/scikit-learn/sklearn_joblib_example.md) - Demonstrates ClearML automatically logging the models created with joblib and a scatter plot created by Matplotlib.
+* [scikit-learn with Matplotlib](../guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) - Demonstrates ClearML automatically logging scatter diagrams created with Matplotlib.
 
 
 ## Remote Execution
diff --git a/docs/integrations/transformers.md b/docs/integrations/transformers.md
index 360b19d7..f3a4306d 100644
--- a/docs/integrations/transformers.md
+++ b/docs/integrations/transformers.md
@@ -28,7 +28,7 @@ All you have to do is install and set up ClearML:
 That’s it! In every training run from now on, the ClearML experiment 
 manager will capture:
 * Source code and uncommitted changes
-* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments),
+* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments)
 and TensorFlow definitions
 * Installed packages
 * Model files (make sure the `CLEARML_LOG_MODEL` environment variable is set to `True`)
diff --git a/docs/model_registry.md b/docs/model_registry.md
index 574526af..8115592c 100644
--- a/docs/model_registry.md
+++ b/docs/model_registry.md
@@ -35,7 +35,7 @@ continue to train and test new model versions.
 ClearML supports automatic and manual registration of models to the model catalog. 
 
 ### Automatic Logging
-ClearML automatically logs models created/loaded through popular frameworks like TensorFlow or Scikit-Learn; all you 
+ClearML automatically logs models created/loaded through popular frameworks like TensorFlow or scikit-learn; all you 
 need to do is [instantiate a ClearML Task](clearml_sdk/task_sdk.md#task-creation) in your code. ClearML stores the 
 framework's training results as output models.
 
diff --git a/docs/pipelines/pipelines_sdk_tasks.md b/docs/pipelines/pipelines_sdk_tasks.md
index 01618328..a1fd8dac 100644
--- a/docs/pipelines/pipelines_sdk_tasks.md
+++ b/docs/pipelines/pipelines_sdk_tasks.md
@@ -52,7 +52,10 @@ For files, call `connect_configuration()` before reading the configuration file.
 path.
 
 ```python
-config_file = pipe.connect_configuration(configuration=config_file_path, name="My Configuration", description="configuration for pipeline")
+config_file = pipe.connect_configuration(
+    configuration=config_file_path, 
+    name="My Configuration", description="configuration for pipeline"
+)
 my_params = json.load(open(config_file,'rt'))
 ```
 
diff --git a/docs/webapp/datasets/webapp_dataset_viewing.md b/docs/webapp/datasets/webapp_dataset_viewing.md
index ef106e12..ada1a7ab 100644
--- a/docs/webapp/datasets/webapp_dataset_viewing.md
+++ b/docs/webapp/datasets/webapp_dataset_viewing.md
@@ -116,7 +116,7 @@ clicking the checkbox in the top left corner of the list.
 Click the checkbox in the top left corner of the list to select all items currently visible.
 
 An extended bulk selection tool is available through the down arrow next to the checkbox in the top left corner, enabling selecting items beyond the items currently on-screen:
-* All - Select all versions in the dataset
-* None - Clear selection
-* Filtered - Select all versions in the dataset that match the current active filters
+* **All** - Select all versions in the dataset
+* **None** - Clear selection
+* **Filtered** - Select all versions in the dataset that match the current active filters