This 2-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
At the end of this course, participants will be able to:
• Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
• Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
• Employ BigQuery and Cloud Datalab to carry out interactive data analysis
• Choose between Cloud SQL, BigTable and Datastore
• Train and use a neural network using TensorFlow
• Choose between different data processing products on the Google Cloud Platform
Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:
• A common query language such as SQL
• Extract, transform, load activities
• Data modeling
• Machine learning and/or statistics
• Programming in Python
Google Account Notes:
• Google services are currently unavailable in China.
New! CERTIFICATE COMPLETION CHALLENGE to unlock benefits from Coursera and Google Cloud
Enroll and complete Cloud Engineering with Google Cloud or Cloud Architecture with Google Cloud Professional Certificate or Data Engineering with Google Cloud Professional Certificate before November 8, 2020 to receive the following benefits;
=> Google Cloud t-shirt, for the first 1,000 eligible learners to complete. While supplies last. > Exclusive access to Big => Interview ($950 value) and career coaching
=> 30 days free access to Qwiklabs ($50 value) to earn Google Cloud recognized skill badges by completing challenge quests
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 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.
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.
-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.
Y. Nicodeme completed this course, spending 5 hours a week on it and found the course difficulty to be easy.
This course materials and in particular its organization were very deceptive. Google Cloud platform looks like a great platform, but the tutorials and examples are no different from what you'd fine by opening a free account at Google Cloud platform.