Machine Learning/Deepdive course missing code

Hi!

Reviewing your courses I found out that the [notebook ](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive/10_recommend/hybrid_recommendations/hybrid_recommendations_preproc.ipynb)for hybrid recommendations states:

`First, let's create our hybrid dataset query that we will use in our Cloud Dataflow pipeline. This will combine some content-based features and the user and item embeddings learned from our WALS Matrix Factorization Collaborative filtering lab that we extracted from our trained WALSMatrixFactorization Estimator and uploaded to BigQuery.`

And the problem is that the previous [notebook ](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive/10_recommend/wals.ipynb)where you go through the WALS factorization doesn't mention uploading this items embeddings to bigquery.

Since the table is already uploaded we don't neccesary have to create it but the notebook misses how to create a file we can later map with the original dataset and assign to each user by item factors and user factors. This dataset being the one used for hybrid recommendations.

Could you provide the code on how to create this table? or at least the information that is inside the table.

Find here the especific join you do with the table containing this data.

> LEFT JOIN
> `cloud-training-demos.GA360_test.user_factors` AS user_factors
> ON CAST(training_dataset.visitor_id AS FLOAT64) = CAST(user_factors.user_id AS FLOAT64)
> LEFT JOIN
> `cloud-training-demos.GA360_test.item_factors` AS item_factors
> ON CAST(training_dataset.content_id AS STRING) = CAST(item_factors.item_id AS STRING)

There is no way to reproduce this table content from what I have found in your notebooks, please correct me if I'm wrong and point me where the code is.

Best.