training-data-analyst | Labs and demos for courses for GCP Training ( http | Learning library

This isn't Python code. These are Unix commands, not meant to be ran in Windows, which appears that you are.

You shouldn't need to modify permissions of example data, and Python has native functions for listing contents of directory and files that you should be using instead.

For example, os.glob() and open()

Based on the usage of /home/jupyter, I would guess you skipped part of the tutorial that is using a Docker container

In the example you have shared, the goal is to fit a linear regression to the discovered factor values so that a novel set of factor values can be used to predict the rating. In this kind of setup, you don't want information about which samples are being used; the only crucial information is the training features (the factor scores) and the rating (the training/test label). For more on this topic, take a look at "Dimensionality reduction using non-negative matrix factorization for information retrieval."

If you included the movie ids and user ids in as features, your regression would try to learn on those, which would either add noise to the model or learn that low ids = lower score etc. This is possible, especially if this ids are in some kind of order you're not aware of, such as chronological or by genre.

Note: You could use movie-specific or user-specific information to build a model, but you would have many, many dimensions of data, and that tends to create poorly performing models. The idea here is to avoid the problem of dimensionality by first reducing the dimensionality of the problem space. Matrix factorization is just one method among many to do this. See, for example, PCA, LDA, and word2vec.

The training-data-analyst repository you mentioned, also has the solutions to all the notebooks.

From analysing the provided solution it looks like the def fn() part is reduntant. the read_dataset function should simply return a tf.Data.dataset: