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Google云平臺(tái)大數(shù)據(jù)與機(jī)器學(xué)習(xí)基礎(chǔ)培訓(xùn)

Introduction to the Data and Machine Learning on Google Cloud Platform Specialization .

Welcome to the Big Data and Machine Learning fundamentals on GCP course.

Here you will learn the basics of how the course

is structured and the four main big data challenges you will solve for.

Recommending Products using Cloud SQL and Spark

In this module you will have an existing Apache SparkML recommendation model that is running on-premise.

You will learn about recommendation models and how

you can run them in the cloud with Cloud Dataproc and Cloud SQL.

Predict Visitor Purchases with BigQuery ML

In this module, you will learn the foundations of BigQuery and big data analysis at scale.

You will then learn how to build your own custom machine learning model

to predict visitor purchases using just SQL with BigQuery ML.

Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow

In this module you will engineer and build an auto-scaling streaming data pipeline to ingest,

process, and visualize data on a dashboard. Before you build your pipeline you'll learn the foundations

of message-oriented architecture and pitfalls to avoid when designing and implementing modern data pipelines.

Classify Images with Pre-Built Models using Vision API and Cloud AutoML

Don't want to create a custom ML model from scratch? Learn how to leverage and extend pre-built

ML models like the Vision API and Cloud AutoML for image classification.

Summary

In this final module, we will review the key challenges,

solutions, and topics covered as part of this fundamentals course.

We will also review additional resources and the steps you can take to get certified as a Google Cloud Data Engineer.