In this hands-on lab explore the Vision, Speech-to-Text, Translation, and Natural Language APIs and use the APIs to analyse audio recordings and map them to relevant images.
In this lab you will learn how to use Google Cloud Machine Learning and Tensorflow to develop and evaluate prediction models using machine learning.
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform.
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.
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.
In this lab you’ll build a simple scikit-learn model, upload the model to Cloud Machine Learning Engine, and make predictions against the model.
Using Cloud DataProc running on a Hadoop cluster you will analyse a data set using Bayes Classification.
Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn machine learning and TensorFlow concepts and develop hands-on skills in developing, evaluating, and productionizing machine learning models.
In this lab you will build an end to end machine learning solution using Tensorflow + Cloud ML Engine and leverage the cloud for distributed training and online prediction.
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.