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Hands-On Lab

Integrating Machine Learning APIs

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.

Hands-On Lab

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.

Course

Big Data and Machine Learning Fundamentals (v1.1)

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform.

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Hands-On Lab

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.

Hands-On Lab

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.

Hands-On Lab

Scikit-learn Model Serving with Online Prediction Using Cloud Machine Learning Engine

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.

Hands-On Lab

Real Time Machine Learning with Google Cloud ML

Using Cloud DataProc running on a Hadoop cluster you will analyse a data set using Bayes Classification.

Course

Introduction to Machine Learning with TensorFlow and Cloud ML Engine

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.

Hands-On Lab

Predict Housing Prices with Tensorflow and Cloud ML Engine

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.

Hands-On Lab

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.

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