Learn Google Cloud
101 expert-rated courses covering Google Cloud. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
The SkillsetCourse catalog offers a comprehensive selection of Google Cloud courses, with all 107 courses providing certificate options. Available on platforms like Google AI / Cloud Skills Boost and Coursera, learners can access one free course. Related skills such as Cloud Console and Deployment are integral to mastering Google Cloud, ensuring a well-rounded educational experience.
Catalog analysis updated . Ratings are independent editorial scores. Read the rating methodology.
Key Facts About Google Cloud
- 1Google Cloud provides a robust platform for cloud services, including data storage and machine learning.
- 2SkillsetCourse offers 107 courses focused on Google Cloud, all of which award certificates.
- 3Courses are available on platforms like Google AI / Cloud Skills Boost, Coursera, and LinkedIn Learning.
- 4One free course is available for learners interested in Google Cloud.
- 5Key related skills include BigQuery, Infrastructure, and Machine Learning.
Top Google Cloud Courses
Google AI Professional Certificate
Earn Google's AI Professional Certificate covering AI fundamentals, machine learning, and responsible AI. Designed for career growth in AI roles.
Advanced Machine Learning on Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
Build and Modernize Applications With Generative AI
This learning path is for application developers who want to enhance their projects with the power of generative AI and accelerate their development workflow. From understanding the core concepts of Gemini, Google's advanced language model, to building end-to-end applications on Google Cloud, this path will guide you through essential techniques and tools. You'll learn how to leverage Gemini Code Assist to streamline your development process, whether you're working with the command-line interface, configuring it for your organization, or kicking off a new project. Finally, dive into hands-on labs to practice what you've learned and earn several skill badges.
Building No-Code Apps with AppSheet
This specialization first introduces you to the fundamentals of no-code application development and the capabilities offered by Google Cloud's no-code application development platform AppSheet. Learn to organize and manage app data, secure and customize apps, and integrate with external services. The courses in this specialization also include topics on managing and upgrading your app, improving app performance and troubleshooting. Recognize the need to implement business process automation, and use AppSheet’s automation capabilities to send notifications, generate reports and parse documents.
Creating Business Value with Data and Looker
This series of courses introduces data in the cloud and Looker to someone who would like to become a Looker Developer. It includes the background on how data is managed in the cloud and how it can be used to create value for an organization. You will then learn the skills you need as a Looker Developer to use the Looker Modeling Language (LookML) to empower your organization to conduct self-serve data exploration, analysis and visualization.
Data Analytics and Visualization
This learning path provides a comprehensive introduction to the data lifecycle, focusing on how to derive actionable insights using Google Cloud’s powerful analytics tools. Learners will progress from foundational cloud concepts to advanced data transformation with BigQuery and professional dashboarding in Looker Studio. By the end of this path, you will be able to ingest, clean, and visualize complex datasets to support data-driven decision-making.
Developing Applications with Google Cloud
In this specialization, you learn the fundamentals of application development on Google Cloud. Through a combination of presentations and hands-on labs, participants learn best practices for designing cloud applications, the use of service orchestration and choreography to coordinate microservices, and how to use Cloud Functions to develop single-purpose functions that process events within your cloud infrastructure. This class is intended for application developers, architects, and cloud engineers who want to build new cloud applications or redesign existing applications to run on Google Cloud. This course teaches participants the following skills: Understand how to choose the appropriate data storage option for application use cases. Use authentication and authorization to secure an application. Describe use cases for the different Google Cloud compute options for running applications. Describe the benefits and challenges of microservice-based architectures. Describe the advantages of event-driven applications. Identify the strengths of orchestration and choreography. Use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to coordinate a microservices application on Google Cloud. Recognize the benefits of and use cases for Cloud Functions in modern application development. Understand how to build, test, and deploy Cloud Functions. Secure and connect Cloud Functions to resources and cloud databases. Use best practices with Cloud Functions.
Digital Transformation Using AI/ML with Google Cloud
This series of courses begins by introducing fundamental Google Cloud concepts to lay the foundation for how businesses use data, machine learning (ML), and artificial intelligence (AI) to transform their business models. The specialization is intended for anyone interested in how the use of AI and ML for the cloud, and especially for data, creates opportunities and requires change for businesses. No previous experience with ML, programming, or cloud technologies is required. The courses do not include any hands-on technical training.
Gemini in BigQuery
This learning path provides a journey into leveraging Gemini within BigQuery for advanced data and AI workflows. Starting with foundational productivity enhancements, it progresses to building generative AI applications and culminates in mastering Retrieval Augmented Generation to mitigate AI inaccuracies. By completing this path, learners will gain practical skills in utilizing Gemini to streamline data processes, create innovative AI solutions, and ensure reliable AI outputs within the BigQuery environment.
Google Cloud Database Engineer
A Database Engineer designs, creates, manages, migrates, and troubleshoots databases used by applications to store and retrieve data. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the Database Engineer role. Once you complete the path, check out our catalog for 700+ labs and courses to keep going on your professional journey.
Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs
In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion. You will start learning the basics of BigQuery, building and optimizing warehouses, and then get hands-on practice on the more advanced data integration features available in Cloud Data Fusion. Learning will take place leveraging Google Cloud's Qwiklab platform where you will have the virtual environment and resources need to complete each lab. This specialization is broken up into 4 courses comprised of a series of courses: BigQuery Basics for Data Analysts Build and Optimize Data Warehouses with BigQuery Building Advanced Codeless Pipelines on Cloud Data Fusion Data Science on Google Cloud: Machine Learning You will even be able to earn a Skills Badge in one of these lab-based courses.
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
Machine Learning Operations (MLOps) on Google Cloud
This learning path is designed for data scientists and ML engineers looking to bridge the gap between machine learning prototypes and production-ready systems on Google Cloud. Learners will explore the full MLOps lifecycle, including feature management with Vertex AI Feature Store, robust model evaluation for predictive and generative AI, and the orchestration of automated workflows. The path concludes with advanced training on building production-grade pipelines using the Kubeflow SDK, Google Cloud components, and AI-driven development with the Data Science Agent.
Managing Google Cloud's Apigee API Platform for Hybrid Cloud
In this specialization, you will learn the Apigee hybrid architecture, and develop an understanding of the Apigee hybrid terminology and organizational model. You will install Apigee hybrid, and implement scenarios to manage, scale, and monitor the software components that make up the hybrid platform. By completing this specialization, you will help accelerate customer time to value, minimize customer dependency on PSO and support for Apigee hybrid, and enable customers to scale up the platform as their API programs grow.
Preparing for Google Cloud Certification: Cloud Engineer
Cloud Engineer plans, configures, sets up, and deploys cloud solutions. This specialization provides the practical skills required for this role, preparing you to successfully manage enterprise solutions on Google Cloud. You will gain real-world, applied experience with Google Cloud technologies. This specialization focuses on the essential skills for the Cloud Engineering role, from infrastructure setup to application deployment.
Professional Cloud Architect Part 1: Infra & Network
The Professional Cloud Architect curriculum has been split into two targeted parts to provide a more focused learning experience. This specialization (Part 1) has been updated to prioritize deep-dive infrastructure and networking. We have replaced general networking overviews with three specialized courses covering Network Architecture, Hybrid/Multicloud, and the Network Intelligence Center. Implementation-heavy topics like GKE and specific exam-prep modules have been moved to Part 2 to ensure a logical progression from architectural design to operational engineering. This is Part 1 of a two-part series designed to prepare you for the Professional Cloud Architect certification. Part 1 focuses on the design and architecture of infrastructure and global networks, while Part 2 covers application modernization, observability, and final exam preparation. Please note that taking both parts is not required for general learning; however, completing both is highly recommended if you are planning on taking the Professional Cloud Architect certification exam.
Professional Cloud Architect Part 2: AI, Security, & Ops
This learning path explores the advanced pillars of the Google Cloud Professional Cloud Architect certification. It covers infrastructure automation with Terraform, comprehensive security management, and scaling modern workloads with GKE and Cloud Run. You will also learn to integrate AI/ML and data engineering capabilities while mastering the observability tools required to ensure enterprise-grade reliability.
Security & Compliance on Google Cloud
This learning path is designed for security professionals and cloud architects aiming to master the Google Cloud security ecosystem. Learners will progress from foundational AI security frameworks (SAIF) and autonomic security operations to the management of vulnerabilities and risks, focusing on high-impact security operations use cases within Security Command Center. The path concludes with a deep dive into Google SecOps, equipping students to engineer advanced detection rules (SIEM) and automate incident response (SOAR).
Vertex AI Search for Retail
This learning path will showcase the skills needed to build dataflows, initial machine learning patterns and utilization of Vertex AI Search for Retail in pursuit of increased retail search potential. The learning path includes courses and labs that will let a learner work in the data space surrounding Vertex AI Search for Retail and then get hands on to practice with the product itself.
Accelerate App Development with Gemini CLI
"This course is designed for app developers and DevOps engineers who want to work smarter by using Gemini CLI, a generative AI agent made for the terminal and powered by Gemini. This course discusses Gemini CLI installation and configuration, and introduces use cases and security best practices. It explains commands, tools, MCP servers, and extensions.
+ 81 more courses available
Pro Tips for Learning Google Cloud
- #1Start with the 'Google Introduction to AI' course to build foundational knowledge in Google Cloud.
- #2Utilize the free course option to explore Google Cloud without financial commitment.
- #3Practice using Cloud Console and Self-paced Labs to gain hands-on experience.
- #4Consider following up with advanced courses like 'Architecting with Google Compute Engine' for deeper insights.
Why Learn Google Cloud?
- Learning Google Cloud enhances career opportunities in cloud computing and data management.
- Mastering Google Cloud can lead to roles in machine learning and AI development.
- Understanding Google Cloud is essential for businesses transitioning to cloud-based infrastructures.
- Proficiency in Google Cloud tools can improve project efficiency and innovation.