virtual-env.ipynb: review fixes 1

Author: danykaufman

Diff for: virtual-env.ipynb

@@ -45,17 +45,17 @@
     "<a id=\"preconfigured-conda-environments\"></a>\n",
     "## Preconfigured Conda environments\n",
     "\n",
-    "Iguazio provides several preconfigured Conda environments that are included in the Jupyter pod:\n",
-    "- **base**: This is the default base environment that includes the Conda binaries. It should not be used for development purposes.\n",
-    "- **jupyter**: This environment incudes the JupyterLab server and all of its dependencies. It should not be used for development purposes.\n",
+    "The platform provides several preconfigured Conda environments that are included in the Jupyter pod:\n",
+    "- **base**: This is the default base environment that includes the Conda binaries. Do not use it for development purposes.\n",
+    "- **jupyter**: This environment incudes the JupyterLab server and all of its dependencies. Do not use it for development purposes.\n",
     "- **mlrun-base**: This environment includes MLRun and all its dependencies preinstalled.\n",
     "- **mlrun-extended**: This environment includes all the packages from mlrun-base, as well as additional packages such as TensorFlow, PyTorch, and scikit-learn, which are required for the demo notebooks.\n",
     "\n",
-    "While Iguazio provides several preconfigured Conda environments that are included in the Jupyter pod, it's important to note that any Conda installations to these environments are not persistent. They will be reset when the Jupyter service is restarted.\n",
+    "While the platform provides several preconfigured Conda environments that are included in the Jupyter pod, it's important to note that any Conda installations to these environments are not persistent. They are reset when the Jupyter service is restarted.\n",
     "\n",
-    "However, when these Conda environments are activated, the `PIP_PREFIX` and `PYTHONPATH` environment variables are automatically set to the data mount directory of the running user: `/User/.pythonlibs/<environment name>`. This means that PIP installations will persist even after restarting the Jupyter pod.\n",
+    "However, when these Conda environments are activated, the `PIP_PREFIX` and `PYTHONPATH` environment variables are automatically set to the data mount directory of the running user: `/User/.pythonlibs/<environment name>`. This means that PIP installations persist even after restarting the Jupyter pod.\n",
     "\n",
-    "Any new Conda environment that is created or cloned will be fully located in the data mount directory of the running user, specifically at `/User/.conda/<environment name>`. It is worth noting that these environments are fully persistent."
+    "Any new Conda environment that is created or cloned will be fully located in the data mount directory of the running user, specifically at `/User/.conda/<environment name>`. These environments are fully persistent."
    ]
   },
   {
@@ -93,7 +93,7 @@
     "   ```   \n",
     "   <br>\n",
     "\n",
-    "4. <a id=\"conda-virt-env-setup-step-install-packages\"></a>Once you activate your environment, you can use either PIP or Conda to install the necessary Python packages.<br> To do this, replace `<package>` with the name of the package you want to install, and optionally add `==<version>` for PIP or `=<version>` for Conda. You can specify multiple packages in the same command.<br>\n",
+    "4. <a id=\"conda-virt-env-setup-step-install-packages\"></a>Once you activate your environment, use either PIP or Conda to install the necessary Python packages.<br> To do this, replace `<package>` with the name of the package you want to install, and optionally add `==<version>` for PIP or `=<version>` for Conda. You can specify multiple packages in the same command.<br>\n",
     "\n",
     "   Using PIP:\n",
     "   ```sh\n",
@@ -104,7 +104,7 @@
     "   conda install <package> [<package> ...]\n",
     "   ```\n",
     "   \n",
-    "   <br>For example, the following command uses PIP to install the SciPy, pandas version 1.4.4, and TensorFlow version 2.9.3 packages for the \"myenv\" environment that we activated in the previous step:\n",
+    "   <br>For example, the following command uses PIP to install the SciPy, pandas version 1.4.4, and TensorFlow version 2.9.3 packages for the \"myenv\" environment that you activated in the previous step:\n",
     "   ```sh\n",
     "   pip install scipy pandas==1.4.4 tensorflow==2.9.3\n",
     "   ```\n",
@@ -122,14 +122,14 @@
     "   conda env export -n myenv > /v3io/users/$V3IO_USERNAME/virtual_env/myenv.yaml\n",
     "   ```\n",
     "   \n",
-    "   <br>To shorten this command, you can use the `/User` data mount to the running-user directory (see [Platform Data Containers](data-ingestion-and-preparation/README.ipynb#platform-data-containers)):\n",
+    "   <br>To shorten this command, use the `/User` data mount to the running-user directory (see [Platform Data Containers](data-ingestion-and-preparation/README.ipynb#platform-data-containers)):\n",
     "   ```sh\n",
     "   conda env -n myenv export > /User/virtual_env/myenv.yaml\n",
     "   ```\n",
     "   <br>\n",
     "\n",
     "6. <a id=\"conda-virt-env-setup-step-refresh-ui\"></a>Refresh the JupyterLab UI to apply your changes.<br>\n",
-    "   After refreshing the UI, you should be able to see your new environment in the list of available kernels in JupyterLab."
+    "   After refreshing the UI, you should see your new environment in the list of available kernels in JupyterLab."
    ]
   },
   {