Google Cloud Platform Big Data and Machine Learning Fundamentals (Coursera)

This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

- Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

- Data Engineering, Big Data, and Machine Learning on GCP Specialization

- Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

What You Will Learn

- Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.

- Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.

- Employ BigQuery to carry out interactive data analysis.

- Choose between different data processing products on Google Cloud.

Syllabus

WEEK 1

Introduction to the Data and Machine Learning on Google Cloud Course

Welcome to the Big Data and Machine Learning fundamentals on Google Cloud 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 Using 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.

WEEK 2

Real-time IoT Dashboards with Pub/Sub, Dataflow, and Data Studio

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.

Deriving Insights from Unstructured Data using Machine Learning

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.