chore: updates notebook title and removes section numbers (#2388)

Author: jeffonelson

gemini/use-cases/applying-llms-to-data/ai-assisted-data-science/ai-assisted-data-science.ipynb

@@ -31,7 +31,7 @@
         "id": "9Ku6cXXnwATV"
       },
       "source": [
-        "# Multimodal Analysis and Vector Search with BigQuery ML\n",
+        "# AI-Assisted Data Science Workflows in BigQuery\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td style=\"text-align: center\">\n",
@@ -334,7 +334,7 @@
       },
       "source": [
         "---\n",
-        "## 1. Data Preparation and Feature Engineering\n",
+        "## Data Preparation and Feature Engineering\n",
         "\n",
         "### Create a BigQuery dataset\n",
         "\n",
@@ -443,7 +443,7 @@
         "SELECT\n",
         "  *,\n",
         "  EXTRACT(YEAR FROM CURRENT_DATE()) - year_built AS property_age,\n",
-        "  OBJ.FETCH_METADATA(OBJ.MAKE_REF(house_uri, 'us.ai_connection')) as image_ref\n",
+        "  OBJ.FETCH_METADATA(OBJ.MAKE_REF(house_uri, 'us.ai_connection')) AS image_ref\n",
         "FROM\n",
         "  `housing_dataset.listings`\n",
         "WHERE sale_status = 'For Sale';"
@@ -457,7 +457,7 @@
       },
       "source": [
         "---\n",
-        "## 2. Multimodal Enrichment with AI Functions\n",
+        "## Multimodal Enrichment with AI Functions\n",
         "\n",
         "### Create a remote model for Gemini\n",
         "\n",
@@ -578,7 +578,7 @@
       },
       "source": [
         "---\n",
-        "## 3. Model Training with K-Means Clustering\n",
+        "## Model Training with K-Means Clustering\n",
         "\n",
         "With your enriched and feature-engineered dataset, you can now build a machine learning model. Your goal is to segment the house listings into distinct groups based on their characteristics. K-means clustering is an unsupervised learning algorithm that groups data points based on their similarity.\n",
         "\n",
@@ -621,7 +621,7 @@
       },
       "source": [
         "---\n",
-        "## 4. Model Evaluation and Prediction\n",
+        "## Model Evaluation and Prediction\n",
         "\n",
         "### Evaluate the clustering model\n",
         "\n",
@@ -713,7 +713,7 @@
         "---\n",
         "\n",
         "\n",
-        "## 5. Visualize and Interpret Clusters\n",
+        "## Visualize and Interpret Clusters\n",
         "\n",
         "With each property now assigned to a cluster, you can visualize the results to better understand the distinct housing segments."
       ]
@@ -824,7 +824,7 @@
       },
       "source": [
         "---\n",
-        "## 6. Generate Cluster Descriptions with Gemini\n",
+        "## Generate Cluster Descriptions with Gemini\n",
         "\n",
         "While the centroids and visualizations are useful, you can use generative AI to create human-readable descriptions for each cluster.\n",
         "\n",
@@ -997,7 +997,7 @@
       },
       "source": [
         "---\n",
-        "## 7. Automate Modeling with the Data Science Agent\n",
+        "## Automate Modeling with the Data Science Agent\n",
         "\n",
         "Now, you'll explore a different approach to the same problem. Instead of writing SQL and Python code manually, you will use the integrated [**Data Science Agent**](https://cloud.google.com/bigquery/docs/colab-data-science-agent) to automatically generate code for a K-means clustering model.\n",
         "\n",
@@ -1021,7 +1021,7 @@
       },
       "source": [
         "---\n",
-        "## 8. Multimodal Search with Embeddings and Vector Search\n",
+        "## Multimodal Search with Embeddings and Vector Search\n",
         "\n",
         "In this final section, you will use a multimodal embedding model to convert images into [vector embeddings](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding#embeddings) and then use BigQuery's built-in [vector search](https://cloud.google.com/bigquery/docs/vector-search-intro) capabilities to find similar houses based on a text description or another image."
       ]
@@ -1313,7 +1313,7 @@
         "---\n",
         "\n",
         "\n",
-        "## 9. Cleaning Up\n",
+        "## Cleaning Up\n",
         "\n",
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",