Merge pull request #245 from v3io/development

getting-started-tutorial minor spelling and grammar fixes

Author: gilad-shaham

getting-started-tutorial/getting-started-tutorial.ipynb

@@ -16,7 +16,7 @@
     "## Overview\n",
     "\n",
     "This tutorial is designed to demonstrate some of the main capabilities of the Iguazio Data Science Platform (**\"the platform\"**).\n",
-    "The tutorial walks yo through a basic machine-learning (ML) workflow, which includes the following steps; before running the steps, ensure that the required [prerequisites](#gs-prerequisites) are met:\n",
+    "The tutorial walks you through a basic machine-learning (ML) workflow, which includes the following steps; before running the steps, ensure that the required [prerequisites](#gs-prerequisites) are met:\n",
     "\n",
     "1. [Create and configure a new project](#gs-step-project-create-n-config)\n",
     "2. [Ingest a data set](#gs-step-ingest-data)\n",
@@ -1331,7 +1331,7 @@
    "metadata": {},
    "source": [
     "<a id=\"gs-explore-data-w-mlrun-func\"></a>\n",
-    "### Explore the Data with an MLRun Markeplace Function\n",
+    "### Explore the Data with an MLRun Marketplace Function\n",
     "\n",
     "You can perform further data exploration by leveraging **the MLRun functions marketplace** (a.k.a. \"the MLRun functions hub\").\n",
     "This marketplace is a centralized location for open-source contributions of function components that are commonly used in machine-learning development.\n",
@@ -1758,7 +1758,7 @@
     "## Step 4: Run Model Training at Scale\n",
     "\n",
     "It's time to train your model.\n",
-    "Start by adding a training project function that uses the [`sklearn_classifier` MLRun marketplace function](https://github.com/mlrun/functions/tree/master/sklearn_classifier) to train three scikit-learn models; (you can use this function to train any sciki-learn model).\n",
+    "Start by adding a training project function that uses the [`sklearn_classifier` MLRun marketplace function](https://github.com/mlrun/functions/tree/master/sklearn_classifier) to train three scikit-learn models; (you can use this function to train any scikit-learn model).\n",
     "Then, use an MLRun task to run your function in order to train and evaluate the models and select the optimal model."
    ]
   },
@@ -1802,14 +1802,14 @@
     "The returned run object is stored in a `train_run` variable.\n",
     "\n",
     "The code uses the `with_hyper_params` task method, which enables running multiple iterations of the same task with different parameter combinations, comparing the results, and selecting the optimal combination based on the specified selection criteria.\n",
-    "In this example, there's a single models parameter with different values (model names) so as to compare multiple models; the model with the most accurate training results is selected as the optimal model.\n",
+    "In this example, there's a single `models` parameter with different values (model names) so as to compare multiple models; the model with the most accurate training results is selected as the optimal model.\n",
     "The `with_hyper_params` method receives the following parameters\n",
     "\n",
     "- **`hyperparams`** &mdash; a dictionary of parameter names and parameter-values lists.\n",
     "  The example defines a single hyperparameter named `model_pkg_class`, and sets its value to a `models` variable that contains a list of three scikit-learn models to train and evaluate. \n",
-    "- **`selector`** &mdash; a string of the format `'<min/max>.<validation criterion>'`, which defines a selection criteria for selecting the winning run iteration.\n",
+    "- **`selector`** &mdash; a string of the format `'<min/max>.<validation criterion>'`, which defines a selection criterion for selecting the winning run iteration.\n",
     "  For example, `\"max.accuracy\"` selects the iteration with the most accurate results while `\"min.loss\"` selects the iteration with minimum loss.\n",
-    "  The examples uses the `max.accuracy` selector to select the model with the most accurate training results."
+    "  The example uses the `max.accuracy` selector to select the model with the most accurate training results."
    ]
   },
   {
@@ -2634,7 +2634,7 @@
     "The workflow file has two parts &mdash; initialization of the function objects, and definition of a pipeline DSL (domain-specific language) for connecting the function inputs and outputs.\n",
     "Examine the code to see how functions objects are initialized and used (by name) within the workflow.\n",
     "\n",
-    "The defined pipeline includes the folloiwng steps:\n",
+    "The defined pipeline includes the following steps:\n",
     "\n",
     "- Ingest the Iris flower data set (`ingest`).\n",
     "- Analyze the data set (`describe`).\n",
@@ -3205,4 +3205,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}