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Google Cloud Training
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Register for this course and see more details by visiting:
OpenCourser.com/course/8t6zm5/google
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What's inside
Syllabus
Course Introduction
This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
Read more
Syllabus
Course Introduction
This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
Read more
Big Data and Machine Learning on Google Cloud
This section explores the key components of Google Cloud's infrastructure. It's here that we introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
Data Engineering for Streaming Data
This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Looker Studio.
Big Data with BigQuery
This section introduces learners to BigQuery, Google's fully-managed, serverless data warehouse. It also explores BigQuery ML, and the processes and key commands that are used to build custom machine learning models.
Machine Learning Options on Google Cloud
This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.
The Machine Learning Workflow with Vertex AI
This section focuses on the three key phases--data preparation, model training, and model preparation--of the machine learning workflow in Vertex AI. Learners get the opportunity to practice building a machine learning model with AutoML.
Course Summary
This section reviews the topics covered in the course, and provides additional resources for further learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into the data-to-AI lifecycle and the tools Google Cloud provides to support it, making it valuable for those interested in implementing AI solutions on Google Cloud
Offers practical knowledge on big data and machine learning with a focus on Google Cloud's solutions, benefiting professionals working with Google Cloud or planning to
Provides a comprehensive overview of Google Cloud's data-to-AI capabilities, making it useful for those seeking a broad understanding of Google Cloud's offerings in this domain
Taught by Google Cloud Training, indicating the quality and expertise of the instruction
Covers both theoretical concepts and practical applications, aiming to enhance both knowledge and skills
Requires no prior knowledge of big data or machine learning, making it accessible to a wider audience
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Reviews summary
Big data and ml: fundamentals with gcp
Learners say this course is an overview of how to use Google Cloud Platform (GCP) to process data and apply machine learning (ML). It covers BigQuery, Pub/Sub, AutoML, and more. The hands-on labs in Qwiklabs environments are helpful for practicing with GCP, but some learners found the instructions weren’t always clear. While reviewers say this course is a great starting point, it may be more of a refresher for those already familiar with GCP products.
This course provides a comprehensive intro to GCP services and architectural concepts.
"This course has given me complete knowledge of the Fundamentals of Big Data and ML."
"This course offers decision-makers invaluable insight on how data-driven processes can create value as they are executed in some of the most relevant tools on the google cloud platform."
Learners new to GCP and ML find this course to be a great starting point.
"It's a very Introductory course, you will get a high level overview."
"This course offers decision-makers invaluable insight on how data-driven processes can create value as they are executed in some of the most relevant tools on the google cloud platform."
Learners appreciate the opportunity to apply their new GCP knowledge with hands-on practice in Qwiklabs, though some found the instructions lacking.
"This course helped learn the basics of Google Cloud, Big Data and ML."
"This course has given me complete knowledge of the Fundamentals of Big Data and ML. Also, I got a chance to access real Google Cloud infrastructure for lab works."
Those already familiar with GCP may not learn much from this course.
"it is a really high overview of the Google Cloud offerings."
"The course is just an introduction but it is very useful to understand the Google Cloud solutions to use when AI/ML is involved into the process of modernizing an organization business."
Some learners complain that the labs are too scripted and don’t allow for enough exploration.
"Most of the hands on are some what pre- configured with steps, which prevents learning end to end learning of the flow"
"copy-paste code without explaining what the code actually does."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Google Cloud Big Data and Machine Learning Fundamentals with these
activities:
Build a Glossary of Big Data and Machine Learning Terms
Show steps
Helps solidify your understanding of key concepts and jargon used in the field, which will be invaluable throughout the course.
Browse courses on
Terminology
Show steps
-
Create a document or spreadsheet to capture important terms.
-
Define each term clearly and provide examples of its usage.
Learn the basics of GCP Big Data and ML
Show steps
Review the fundamentals of big data and machine learning on GCP to build a solid foundation for the course.
Browse courses on
Big Data
Show steps
-
Explore the GCP website to learn about its big data and ML offerings
-
Follow tutorials on Coursera or YouTube to get hands-on experience with GCP Big Data and ML services
Big Data and Machine Learning on Google Cloud
Show steps
Review Google Cloud's infrastructure and its components to lay a solid foundation in big data and machine learning principles.
Browse courses on
Big Data
Show steps
-
Explore Google Cloud Platform docs on Google Cloud's infrastructure
-
Review whitepapers on the components of Google Cloud's infrastructure
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review 'Data Science for Business' by Provost and Fawcett
Show steps
Provides a solid introduction to data science concepts and techniques used in a business context, which will build a strong foundation for the course.
View
Data Science para Negócios
on Amazon
Show steps
-
Read Chapters 1-3 to gain an overview of data science, its applications, and the role of data in business.
-
Complete the exercises at the end of each chapter to test your understanding.
Join a study group or online discussion forum
Show steps
Engage with peers to share knowledge, discuss concepts, and get feedback on your understanding.
Browse courses on
Collaboration
Show steps
-
Join a study group organized by your university or online platforms
-
Participate in discussion forums on platforms like Reddit or Coursera to connect with other learners
Complete Coursera's 'Big Data Fundamentals' Certification
Show steps
Covers the foundational concepts of big data and provides hands-on practice with data ingestion, processing, and storage, which will complement the course content.
Browse courses on
Big Data
Show steps
-
Enroll in the Coursera certification and complete the 'Introduction to Big Data' course.
-
Complete the 'Data Engineering with Hadoop and Spark' course to gain hands-on experience with big data tools.
Complete the 'Google Cloud Vertex AI Essentials' Tutorial
Show steps
Provides a guided introduction to Google Cloud's Vertex AI platform, which will facilitate your learning in the machine learning modules of the course.
Browse courses on
Vertex AI
Show steps
-
Visit the Google Cloud Vertex AI documentation.
-
Work through the 'Essentials' tutorial to gain hands-on experience with Vertex AI's features.
Practice data manipulation with BigQuery
Show steps
Gain proficiency in using BigQuery for data manipulation, which is essential for building ML models.
Browse courses on
BigQuery
Show steps
-
Complete the BigQuery tutorial on the Google Cloud website
-
Solve practice problems on platforms like LeetCode or HackerRank to test your BigQuery skills
Follow the 'BigQuery ML Crash Course' from Google Cloud
Show steps
Provides a concise and practical introduction to machine learning with BigQuery, which will prepare you for the machine learning modules in the course.
Browse courses on
BigQuery ML
Show steps
-
Visit the Google Cloud website and access the 'BigQuery ML Crash Course'.
-
Work through the interactive tutorial to gain hands-on experience with building and training machine learning models in BigQuery.
Data Engineering for Streaming Data Simulation
Show steps
Simulate an end-to-end data pipeline by working through examples of data ingestion, processing, and visualization using Google Cloud's products and services.
Browse courses on
Data Engineering
Show steps
-
Create a Pub/Sub topic and subscribe to it
-
Build a Dataflow pipeline to process incoming messages
-
Create a Looker dashboard to visualize the processed data
Assist Classmates in the Course Forum
Show steps
Contributes to the learning community and reinforces your understanding by helping others grasp the course concepts.
Browse courses on
Collaboration
Show steps
-
Actively participate in the course discussion forum.
-
Provide thoughtful responses to classmates' questions and share your insights.
Build a simple ML model with Vertex AI
Show steps
Apply your understanding of ML by creating a practical model using Vertex AI, enhancing your hands-on experience.
Browse courses on
Vertex AI
Show steps
-
Choose a dataset from the Vertex AI library or import your own
-
Train a model using AutoML or custom code
-
Evaluate the performance of your model
Develop a Data Pipeline for a Business Case
Show steps
Allows you to apply the data engineering concepts learned in the course to a real-world scenario, solidifying your understanding.
Browse courses on
Data Engineering
Show steps
-
Identify a business problem that can be solved using data.
-
Design a data pipeline to ingest, process, and analyze the required data.
-
Implement the data pipeline using a tool of your choice (e.g., Apache Beam, Airflow).
-
Present your solution and findings to the class or a mentor for feedback.
Build a Machine Learning Model with AutoML
Show steps
Put your machine learning skills into practice by building a regression, classification, or forecasting model using AutoML's user-friendly interface and guided workflows.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset and define your target variable
-
Select an AutoML model type
-
Upload your data and train the model
-
Evaluate the model's performance
-
Deploy and serve the model
Build a Glossary of Big Data and Machine Learning Terms
Show steps
Helps solidify your understanding of key concepts and jargon used in the field, which will be invaluable throughout the course.
Browse courses on
Terminology
Show steps
Show steps
Show steps
- Create a document or spreadsheet to capture important terms.
- Define each term clearly and provide examples of its usage.
Learn the basics of GCP Big Data and ML
Show steps
Review the fundamentals of big data and machine learning on GCP to build a solid foundation for the course.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Explore the GCP website to learn about its big data and ML offerings
- Follow tutorials on Coursera or YouTube to get hands-on experience with GCP Big Data and ML services
Big Data and Machine Learning on Google Cloud
Show steps
Review Google Cloud's infrastructure and its components to lay a solid foundation in big data and machine learning principles.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Explore Google Cloud Platform docs on Google Cloud's infrastructure
- Review whitepapers on the components of Google Cloud's infrastructure
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review 'Data Science for Business' by Provost and Fawcett
Show steps
Provides a solid introduction to data science concepts and techniques used in a business context, which will build a strong foundation for the course.
View
Data Science para Negócios
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to gain an overview of data science, its applications, and the role of data in business.
- Complete the exercises at the end of each chapter to test your understanding.
Join a study group or online discussion forum
Show steps
Engage with peers to share knowledge, discuss concepts, and get feedback on your understanding.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Join a study group organized by your university or online platforms
- Participate in discussion forums on platforms like Reddit or Coursera to connect with other learners
Complete Coursera's 'Big Data Fundamentals' Certification
Show steps
Covers the foundational concepts of big data and provides hands-on practice with data ingestion, processing, and storage, which will complement the course content.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Enroll in the Coursera certification and complete the 'Introduction to Big Data' course.
- Complete the 'Data Engineering with Hadoop and Spark' course to gain hands-on experience with big data tools.
Complete the 'Google Cloud Vertex AI Essentials' Tutorial
Show steps
Provides a guided introduction to Google Cloud's Vertex AI platform, which will facilitate your learning in the machine learning modules of the course.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Visit the Google Cloud Vertex AI documentation.
- Work through the 'Essentials' tutorial to gain hands-on experience with Vertex AI's features.
Practice data manipulation with BigQuery
Show steps
Gain proficiency in using BigQuery for data manipulation, which is essential for building ML models.
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Complete the BigQuery tutorial on the Google Cloud website
- Solve practice problems on platforms like LeetCode or HackerRank to test your BigQuery skills
Follow the 'BigQuery ML Crash Course' from Google Cloud
Show steps
Provides a concise and practical introduction to machine learning with BigQuery, which will prepare you for the machine learning modules in the course.
Browse courses on
BigQuery ML
Show steps
Show steps
Show steps
- Visit the Google Cloud website and access the 'BigQuery ML Crash Course'.
- Work through the interactive tutorial to gain hands-on experience with building and training machine learning models in BigQuery.
Data Engineering for Streaming Data Simulation
Show steps
Simulate an end-to-end data pipeline by working through examples of data ingestion, processing, and visualization using Google Cloud's products and services.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Create a Pub/Sub topic and subscribe to it
- Build a Dataflow pipeline to process incoming messages
- Create a Looker dashboard to visualize the processed data
Assist Classmates in the Course Forum
Show steps
Contributes to the learning community and reinforces your understanding by helping others grasp the course concepts.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Actively participate in the course discussion forum.
- Provide thoughtful responses to classmates' questions and share your insights.
Build a simple ML model with Vertex AI
Show steps
Apply your understanding of ML by creating a practical model using Vertex AI, enhancing your hands-on experience.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Choose a dataset from the Vertex AI library or import your own
- Train a model using AutoML or custom code
- Evaluate the performance of your model
Develop a Data Pipeline for a Business Case
Show steps
Allows you to apply the data engineering concepts learned in the course to a real-world scenario, solidifying your understanding.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify a business problem that can be solved using data.
- Design a data pipeline to ingest, process, and analyze the required data.
- Implement the data pipeline using a tool of your choice (e.g., Apache Beam, Airflow).
- Present your solution and findings to the class or a mentor for feedback.
Build a Machine Learning Model with AutoML
Show steps
Put your machine learning skills into practice by building a regression, classification, or forecasting model using AutoML's user-friendly interface and guided workflows.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset and define your target variable
- Select an AutoML model type
- Upload your data and train the model
- Evaluate the model's performance
- Deploy and serve the model
Career center
Learners who complete Google Cloud Big Data and Machine Learning Fundamentals will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, build, and deploy machine learning models to solve a variety of problems for businesses. This course provides data scientists with a foundation in the tools and services provided by Google Cloud, which can help them build and deploy models more efficiently. The course explores BigQuery ML and the processes and key commands that are used to build custom machine learning models, and it introduces learners to Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects. This course may be particularly useful for data scientists who are new to Google Cloud or who want to learn more about the tools and services that it provides for machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They also work with data scientists to gather and prepare data for modeling. This course provides machine learning engineers with a foundation in the tools and services provided by Google Cloud, which can help them build and deploy models more efficiently. The course explores BigQuery ML and the processes and key commands that are used to build custom machine learning models, and it introduces learners to Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects. This course may be particularly useful for machine learning engineers who are new to Google Cloud or who want to learn more about the tools and services that it provides for machine learning.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that is used to store and process data. This course provides data engineers with a foundation in the tools and services provided by Google Cloud, which can help them build and maintain data infrastructure more efficiently. The course explores an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Looker Studio. This course may be particularly useful for data engineers who are new to Google Cloud or who want to learn more about the tools and services that it provides for data engineering.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course provides data analysts with a foundation in the tools and services provided by Google Cloud, which can help them collect, clean, and analyze data more efficiently. The course explores an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Looker Studio. This course may be particularly useful for data analysts who are new to Google Cloud or who want to learn more about the tools and services that it provides for data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides software engineers with a foundation in the tools and services provided by Google Cloud, which can help them build and maintain software applications more efficiently. The course explores the key components of Google Cloud's infrastructure, and it introduces many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud. This course may be particularly useful for software engineers who are new to Google Cloud or who want to learn more about the tools and services that it provides for software development.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing infrastructure. This course provides cloud architects with a foundation in the tools and services provided by Google Cloud, which can help them design and manage cloud computing infrastructure more efficiently. The course explores the key components of Google Cloud's infrastructure, and it introduces many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud. This course may be particularly useful for cloud architects who are new to Google Cloud or who want to learn more about the tools and services that it provides for cloud architecture.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams. They work to ensure that software is built, tested, and deployed efficiently. This course provides DevOps engineers with a foundation in the tools and services provided by Google Cloud, which can help them build, test, and deploy software more efficiently. The course explores the key components of Google Cloud's infrastructure, and it introduces many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud. This course may be particularly useful for DevOps engineers who are new to Google Cloud or who want to learn more about the tools and services that it provides for DevOps.
Data Warehouse Architect
Data Warehouse Architect
Data Warehouse Architects design and manage data warehouses. This course provides data warehouse architects with a foundation in the tools and services provided by Google Cloud, which can help them design and manage data warehouses more efficiently. The course explores BigQuery, Google's fully-managed, serverless data warehouse, and it introduces learners to BigQuery ML, and the processes and key commands that are used to build custom machine learning models. This course may be particularly useful for data warehouse architects who are new to Google Cloud or who want to learn more about the tools and services that it provides for data warehousing.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers build and maintain the infrastructure that is used to deploy and manage machine learning models. This course provides machine learning operations engineers with a foundation in the tools and services provided by Google Cloud, which can help them build and maintain machine learning infrastructure more efficiently. The course explores the key components of Google Cloud's infrastructure, and it introduces many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud. This course may be particularly useful for machine learning operations engineers who are new to Google Cloud or who want to learn more about the tools and services that it provides for machine learning operations.
For more career information including salaries, visit:
OpenCourser.com/course/8t6zm5/google
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Google Cloud Big Data and Machine Learning Fundamentals.
Practical guide to machine learning. It provides hands-on examples of how to use Scikit-Learn, Keras, and TensorFlow to build machine learning models. The book covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Save
Comprehensive guide to deep learning. It covers the theoretical foundations of deep learning, as well as practical techniques for building and training deep learning models. The book is written by three of the leading researchers in the field of deep learning.
Provides a practical guide to data science. It covers the entire data science process, from data collection and cleaning to model building and evaluation. The book is written by two of the leading researchers in the field of data science.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers the theoretical foundations of machine learning, as well as practical techniques for building and training machine learning models.
Provides a comprehensive overview of reinforcement learning. It covers the theoretical foundations of reinforcement learning, as well as practical techniques for building and training reinforcement learning models.
Provides a comprehensive overview of natural language processing (NLP) with deep learning. It covers the theoretical foundations of NLP, as well as practical techniques for building and training NLP models.
Save
Provides a comprehensive overview of computer vision. It covers the theoretical foundations of computer vision, as well as practical techniques for building and training computer vision models.
Provides a comprehensive overview of robotics. It covers the theoretical foundations of robotics, as well as practical techniques for building and programming robots.
Provides a comprehensive overview of the Fourth Industrial Revolution. It covers the challenges and opportunities of the Fourth Industrial Revolution, as well as the technologies and trends that are shaping it.
Save
Provides a comprehensive overview of the age of AI. It covers the challenges and opportunities of AI, as well as the technologies and trends that are shaping it.
Save
Provides a thought-provoking exploration of the future of AI. It examines the potential benefits and risks of AI, and considers the implications of AI for humanity.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8t6zm5/google
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Effort
2 weeks of study, 6-10 hours/week
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Delves into the data-to-AI lifecycle and the tools Google Cloud provides to support it, making it valuable for those interested in implementing AI solutions on Google Cloud
Offers practical knowledge on big data and machine learning with a focus on Google Cloud's solutions, benefiting professionals working with Google Cloud or planning to
Provides a comprehensive overview of Google Cloud's data-to-AI capabilities, making it useful for those seeking a broad understanding of Google Cloud's offerings in this domain
Taught by Google Cloud Training, indicating the quality and expertise of the instruction
Covers both theoretical concepts and practical applications, aiming to enhance both knowledge and skills
Requires no prior knowledge of big data or machine learning, making it accessible to a wider audience
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