In this lab, you go from exploring a taxicab dataset to training and deploying a high-accuracy distributed model with Cloud ML Engine.

What you need

To complete this lab, you need:

What you learn

In this series of labs, you go from exploring a taxicab dataset to training and deploying a high-accuracy distributed model with Cloud ML Engine.

Step 1

Start Datalab using the instructions in Launch Datalab and come back to this lab.

In this lab, you will:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/datasets/ and open create_datasets.ipynb.

Step 2

In Datalab, click on Clear | All Cells (click on Clear, then in the drop-down menu, select All Cells). Now, read the narrative and execute each cell in turn.

In this lab, you will learn how the TensorFlow Python API works:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/tensorflow and open a_tfstart.ipynb

Step 2

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

In this lab, you will implement a simple machine learning model using tf.learn:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/tensorflow and open b_tflearn.ipynb

Step 2

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

In this lab, you will learn how to:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/tensorflow and open c_batched.ipynb.

Step 2

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

In this lab, you will learn how to:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/tensorflow and open d_experiment.ipynb.

Step 2

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

In this lab, you will learn how to:

Step 1

If you don't already have a bucket on Cloud Storage, create one from the Storage section of the GCP console. Bucket names have to be globally unique.

Step 2

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/cloudmle and open cloudmle.ipynb.

Step 3

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

In this lab, you will improve the ML model using feature engineering. In the process, you will learn how to:

Step 1

In Cloud Datalab, click on the Home icon, and then navigate to training-data-analyst/courses/machine_learning/feateng and open feateng.ipynb.

Step 2

In Datalab, click on Clear | All Cells. Now read the narrative and execute each cell in turn.

Your instructor will demo notebooks that contain hyper-parameter tuning and training on 500 million rows of data. The changes to the model are minor -- essentially just command-line parameters, but the impact on model accuracy is huge:

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