This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
Machine Learning with Spark on Google Cloud Dataproc
In this lab you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset.
Processing Time Windowed Data with Apache Beam and Cloud Dataflow (Java)
Deploy a Java application using Maven to process data with Cloud Dataflow. The Java application implements time-windowed aggregation to augment the raw data in order to produce consistent training and test datasets.
Machine Learning with TensorFlow
In this lab you will learn how to use Google Cloud Machine Learning and Tensorflow to develop and evaluate prediction models using machine learning.
Distributed Machine Learning with Google Cloud ML
Learn the process for partitioning a data set into two separate parts: a training set to develop a model, and a test set to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.