Update README.md

README.md

@@ -47,8 +47,10 @@ To find out how to work with LightAutoML, we have several tutorials:
 2. `Tutorial_2. AutoML pipeline preset.ipynb` - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data. Using presets you can solve binary classification, multiclass classification and regression tasks, changing the first argument in Task.
 3. `Tutorial_3. Multiclass task.ipynb` - shows how to build ML pipeline for multiclass ML task by hand
 
-Each tutorial has the step to enable Profiler and completes with Profiler run, which generates distribution for each function call time and shows it in interactive HTML report: the report show full time of run on its top and interactive tree of calls with percent of total time spent by the specific subtree. 
+Each tutorial has the step to enable Profiler and completes with Profiler run, which generates distribution for each function call time and shows it in interactive HTML report: the report show full time of run on its top and interactive tree of calls with percent of total time spent by the specific subtree.
+
 **Important 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default
+
 **Important 2**: to take a look at this report after the run, please comment last line of demo with report deletion command. 
 
 For more examples, in `tests` folder you can find different scenarios of LAMA usage: