Online Course – Google Cloud Certified Professional Specialization in Machine Learning

Start your career in data engineering at Google Cloud Platform. Learn how to analyze and extract value from big data using big data and machine learning.

Suggested by: Coursera (What is Coursera?)

Start your Coursera.com free trial today
קורס אונליין - התמחות מקצועית מוסמכת בלמידת מכונה של Google Cloud

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Design and create data processing systems on the Google Cloud Platform
  • Use unstructured data with Spark APIs and machine learning in Cloud Dataproc
  • Process data in batch and streaming with the Autoscaler data pipeline implementation in Cloud Dataflow
  • Extract business insights from very large data sets using Google BigQuery
  • Train, evaluate, and predict with machine learning models using TensorFlow and Cloud ML
  • Activate instant insights from streaming data

What you will learn in the course

Courses for which the course is suitable

  • Data processing systems developers
  • Data analysts
  • Data Engineers
  • Machine learning experts
  • Google Cloud Solution Developers
  • Big Data Transformation Managers
  • App developers with unstructured data
  • Google BigQuery Experts
  • Machine learning model developers

Internship – Series of 5 courses

In this intensive, five-week online internship, participants will gain hands-on experience in designing and building data processing systems on the Google Cloud Platform. Through a combination of presentations, demonstrations, and hands-on experiences, participants will learn to design data processing systems, create complete data pipelines and analytics, and develop machine learning solutions. This course focuses on structured, unstructured, and streaming data.

Skills that participants will acquire:

  • Design and create data processing systems on the Google Cloud Platform
  • Use unstructured data with Spark APIs and machine learning in Cloud Dataproc
  • Process data in batch and streaming with the Autoscaler data pipeline implementation in Cloud Dataflow
  • Extract business insights from very large data sets using Google BigQuery
  • Train, evaluate, and predict with machine learning models using TensorFlow and Cloud ML
  • Activate instant insights from streaming data

This class is intended for experienced developers responsible for managing Big Data transformations.

Note: By registering for this internship, you agree to the Qwiklabs Terms of Service as detailed in the FAQ. See the Terms of Service here: Terms of Service

Hands-on Learning Project

This internship includes hands-on labs. You will need a Google account (you can also use a Gmail account), in addition to signing up for a free trial on Google Cloud Platform. The free trial is limited to 12 months or $300, whichever comes first. Therefore, the internship is designed to be completed in four weeks.

With hands-on training, you can apply everything you learned in the video lectures. Projects will include topics such as Google BigQuery, which are used and configured in Codelabs. You will gain practical experience with the concepts learned in the modules.

Details of the courses that make up the specialization

Google Cloud Courses

Course 1: Basics of Big Cloud Data and Machine Learning in Google Cloud

Duration: 10 hours
Rating: 4.7 (99 ratings)

  • Identify the data lifecycle for AI in Google Cloud and the key products of cloud big data and machine learning.
  • Analyze huge data at scale with BigQuery.
  • Identify ways to create machine learning solutions on Google Cloud.
  • Describe a machine learning workflow and key steps with Vertex AI.

Course 2: Reinventing Data Basins and Data Warehouses with GCP

Duration: 8 hours
Rating: 4.8 (22 ratings)

  • Distinguish between data pools and data warehouses.
  • Get to know the use cases for each type of storage and the data sink and data warehouse solutions available in Google Cloud.
  • Understand the role of a data engineer and the benefits of a functional data pipeline for business operations.
  • Analyze why data engineering should be performed in a cloud environment.

Course 3: Building Batch Data Pipelines on GCP

Duration: 17 hours
Rating: 4.7 (15 ratings)

  • Analyze different data loading methods: EL, ELT, and ETL and when to use each one.
  • Run Hadoop on Dataproc, use Cloud Storage, and improve Dataproc’s work.
  • Use Dataflow to create data processing pipelines.
  • Manage data pipelines with Data Fusion and Cloud Composer.

Course 4: Building Resilient Streaming Analytics Systems on GCP

Duration: 10 hours

  • Interpret the use cases of real-time flow analysis.
  • Manage data events using the Pub/Sub asynchronous messaging service.
  • Create flow pipes and perform transformations as needed.
  • Enable interaction between Dataflow, BigQuery, and Pub/Sub to perform real-time streaming and analytics.

Course 5: Smart Analytics, Machine Learning, and AI on GCP

Duration: 7 hours
Rating: 4.7 (11 ratings)

  • Know the difference between ML, AI, and deep learning.
  • Explain the use of ML APIs on unstructured data.
  • Run BigQuery commands in Notebooks.
  • Create ML models using SQL syntax in BigQuery.

Related products