Small edits (#709)

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -24,14 +24,14 @@ The following search strategies can be used:
      
 * Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
 * Full grid sampling strategy of every hyperparameter combination - [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
-* Custom - Use a custom class and inherit from the ClearML automation base strategy class, automation.optimization.SearchStrategy.
+* Custom - Use a custom class and inherit from the ClearML automation base strategy class, [SearchStrategy](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L310)
 
 The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) 
 object later.
 
 The example code attempts to import `OptimizerOptuna` for the search strategy. If `clearml.automation.optuna` is not 
 installed, it attempts to import `OptimizerBOHB`. If `clearml.automation.hpbandster` is not installed, it uses 
-the `RandomSearch` for the search strategy. 
+`RandomSearch` as the search strategy. 
 
 ```python
 try:

docs/guides/reporting/clearml_logging_example.md

@@ -17,7 +17,7 @@ In the ``clearml`` GitHub repository, this example includes a clickable icon to
 
 ## Scalars
 
-To reports scalars, call [Logger.report_scalar()](../../references/sdk/logger.md#report_scalar). 
+To reports scalars, call [`Logger.report_scalar()`](../../references/sdk/logger.md#report_scalar). 
 The scalar plots appear in the **web UI** in **SCALARS**.
 
 ```python
@@ -44,7 +44,7 @@ Plots appear in **PLOTS**.
 
 ### 2D Plots
 
-Report 2D scatter plots by calling [Logger.report_scatter2d()](../../references/sdk/logger.md#report_scatter2d). 
+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 [Logger.report_scatter3d()](../../references/sdk/logger.md#report_scatter3d).
+To plot a series as a 3D scatter plot, use [`Logger.report_scatter3d()`](../../references/sdk/logger.md#report_scatter3d).
 
 ```python
 # report 3d scatter plot
@@ -85,7 +85,7 @@ logger.report_scatter3d(
 
 ![3d scatter plot](../../img/colab_explicit_reporting_05.png)
 
-To plot a series as a surface plot, use [Logger.report_surface()](../../references/sdk/logger.md#report_surface).
+To plot a series as a surface plot, use [`Logger.report_surface()`](../../references/sdk/logger.md#report_surface).
 
 ```python
 # report 3d surface

docs/guides/reporting/explicit_reporting.md

@@ -100,7 +100,7 @@ package contains methods for explicit reporting of plots, log text, media, and t
 First, create a logger for the Task using [`Task.get_logger()`](../../references/sdk/task.md#get_logger):
 
 ```python
-logger = task.get_logger
+logger = task.get_logger()
 ```
 
 ### Plot Scalar Metrics

src/pages/index.js

@@ -22,7 +22,7 @@ const features = [
     imageUrl: 'img/ico-data-science.svg',
     description: (
       <>
-        <code>ClearML</code> is designed to be as easily integrated as possible. <b>Add 2-lines of code</b> and everything is logged &amp; reported!
+        <code>ClearML</code> is designed to be as easily integrated as possible. <b>Add 2 lines of code</b> and everything is logged &amp; reported!
       </>
     ),
   },