Apply suggestions from code review

Co-authored-by: Luis Quintanilla <46974588+luisquintanilla@users.noreply.github.com>

Author: LittleLittleCloud

machine-learning/03-Training and AutoML.ipynb

@@ -400,9 +400,9 @@
       "metadata": {},
       "source": [
         "## Use AutoML to simplify hyper-parameter optimization.\n",
-        "Hyper-parameter optimization is tedious while important, and it's also something that can be done automatically. Using built-in `AutoMLExperiment` can greatly simplify hpo process. `AutoMLExperiment` applies the latest research from MSR so it can conduct swift, accurate and thorough hyper-parameter optimization in a limited time budget.\n",
+        "Hyper-parameter optimization is an important process with lots of trial and error. This process can be automated and simplified using the built-in `AutoMLExperiment`. `AutoMLExperiment` applies the latest research from Microsoft Research to conduct a swift, accurate and thorough hyper-parameter optimization given a limited time budget.\n",
         "\n",
-        "The code below shows how to use `AutoMLExperiment` for hpo and explore a better configuration for `LightGbm` on the non-linear dataset used in Example 2."
+        "The code below shows how to use `AutoMLExperiment` for HPO to explore a better configuration for `LightGbm` on the non-linear dataset used in Example 2."
       ]
     },
     {