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Google Cloud Training
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/00dd8l/smart
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What's inside
Syllabus
Introduction
In this module, we introduce the course and agenda
Introduction to Analytics and AI
This modules talks about ML options on Google Cloud
Read more
Syllabus
Introduction
In this module, we introduce the course and agenda
Introduction to Analytics and AI
This modules talks about ML options on Google Cloud
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores real-world applications, which is standard in industry
Taught by Google Cloud Training, who are recognized for their work in cloud computing
Develops skills in using Google Cloud's AI services, which are core skills for data scientists and analysts
Covers a wide range of topics, from pre-built ML APIs to custom ML model building
Builds a strong foundation for beginners and strengthens an existing foundation for intermediate learners
Requires learners to come in with some background knowledge
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Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Applied ml and ai on google cloud
According to learners, this course provides a strong introduction to using Google Cloud services for analytics, machine learning, and artificial intelligence. Students particularly highlight the value of the hands-on labs which provide practical experience with tools like BigQuery ML, AutoML, and introducing Vertex AI. While many find it a great starting point for applying ML on GCP, some reviewers note it can feel too basic if you have extensive prior ML experience or that prerequisites regarding existing GCP or ML knowledge aren't always clear.
Covers key Google Cloud ML/AI services effectively.
"It provided a great overview of the ML options available on GCP."
"The course introduced me to BigQuery ML, AutoML, and Vertex AI."
"I learned about the different ways to implement ML models on Google Cloud."
"Good coverage of the core tools needed for ML pipelines on GCP."
Offers valuable hands-on practice with GCP tools.
"The labs were incredibly helpful and solidified my understanding."
"The hands-on exercises using BigQuery ML and AutoML were the best part."
"I really appreciated the practical application of concepts through the labs."
"Working directly with GCP services in the labs made everything click."
Some topics could benefit from more detail or slower pace.
"Some sections felt a bit rushed, especially towards the end."
"I wish there was more depth on specific modeling techniques."
"Could benefit from more practical examples or case studies."
"The transition between modules felt a bit abrupt at times."
Mixed views on whether it's for beginners or requires prior knowledge.
"This course assumes some prior knowledge of ML or GCP concepts."
"It was a bit too introductory for someone with existing ML experience."
"I felt lost at times, perhaps it's not for absolute beginners."
"Good if you're new to GCP ML, but not if you expect deep dives."
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 Smart Analytics, Machine Learning, and AI on Google Cloud with these
activities:
Read 'Designing Data-Intensive Applications'
Show steps
Gain insights into designing and managing data-intensive applications that will complement the course's focus on machine learning.
View
Secret Colors
on Amazon
Show steps
-
Read the book and take notes.
-
Apply the concepts to your own data-intensive projects.
Review 'Machine Learning for Hackers'
Show steps
Review fundamental machine learning algorithms and their applications from this introductory book.
View
Machine Learning for Hackers: Case Studies and...
on Amazon
Show steps
-
Read the book and take notes.
-
Complete the practice exercises provided in the book.
Practice Data Preprocessing
Show steps
Use a dataset and apply the data preprocessing techniques covered in the course.
Show steps
-
Explore and clean the data.
-
Transform the data into a suitable format for modeling.
-
Evaluate the effectiveness of your preprocessing.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Machine Learning Model with Vertex AI
Show steps
Follow a tutorial to gain practical experience in deploying a machine learning model using Vertex AI.
Browse courses on
Vertex AI
Show steps
-
Find a relevant tutorial.
-
Follow the tutorial and build the model.
-
Deploy the model and evaluate its performance.
Develop a Machine Learning Pipeline for a Real-World Problem
Show steps
Apply the principles learned in the course to solve a real-world problem using machine learning.
Browse courses on
Machine Learning Pipeline
Show steps
-
Identify a problem and gather data.
-
Develop a machine learning pipeline.
-
Evaluate the pipeline and make improvements.
-
Document and present your findings.
Participate in a Machine Learning Hackathon
Show steps
Test your skills and gain valuable experience by competing in a machine learning hackathon.
Show steps
-
Find a suitable hackathon.
-
Form a team and develop a project.
-
Submit your solution and compete for prizes.
Mentor Junior Data Scientists
Show steps
Reinforce your understanding by mentoring others on the topics covered in the course.
Show steps
-
Identify opportunities to mentor junior data scientists.
-
Share your knowledge and support their learning.
-
Evaluate your mentoring impact.
Read 'Designing Data-Intensive Applications'
Show steps
Gain insights into designing and managing data-intensive applications that will complement the course's focus on machine learning.
View
Secret Colors
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes.
- Apply the concepts to your own data-intensive projects.
Review 'Machine Learning for Hackers'
Show steps
Review fundamental machine learning algorithms and their applications from this introductory book.
View
Machine Learning for Hackers: Case Studies and...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes.
- Complete the practice exercises provided in the book.
Practice Data Preprocessing
Show steps
Use a dataset and apply the data preprocessing techniques covered in the course.
Show steps
Show steps
Show steps
- Explore and clean the data.
- Transform the data into a suitable format for modeling.
- Evaluate the effectiveness of your preprocessing.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Machine Learning Model with Vertex AI
Show steps
Follow a tutorial to gain practical experience in deploying a machine learning model using Vertex AI.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Find a relevant tutorial.
- Follow the tutorial and build the model.
- Deploy the model and evaluate its performance.
Develop a Machine Learning Pipeline for a Real-World Problem
Show steps
Apply the principles learned in the course to solve a real-world problem using machine learning.
Browse courses on
Machine Learning Pipeline
Show steps
Show steps
Show steps
- Identify a problem and gather data.
- Develop a machine learning pipeline.
- Evaluate the pipeline and make improvements.
- Document and present your findings.
Participate in a Machine Learning Hackathon
Show steps
Test your skills and gain valuable experience by competing in a machine learning hackathon.
Show steps
Show steps
Show steps
- Find a suitable hackathon.
- Form a team and develop a project.
- Submit your solution and compete for prizes.
Mentor Junior Data Scientists
Show steps
Reinforce your understanding by mentoring others on the topics covered in the course.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior data scientists.
- Share your knowledge and support their learning.
- Evaluate your mentoring impact.
Career center
Learners who complete Smart Analytics, Machine Learning, and AI on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
For professionals, this course could be an opportunity to strengthen their specialization within Machine Learning Engineering. This course's main focus is teaching learners how to incorporate ML into their data pipelines. Building foundational knowledge in this area through this course will enable learners to improve ML-based data analysis within their organization.
Data Scientist
Data Scientist
For professionals looking to pivot to Data Science from a related field, building a foundational knowledge in ML can enable them to transition to and specialize within Data Science. This course may also help build a foundation for someone in an adjacent role.
Data Analyst
Data Analyst
Data Analysts can use this course to develop foundational knowledge for applying ML techniques to data analysis pipelines within their organization. This foundational knowledge may be necessary for certain subfields of Data Analytics.
AI Engineer
AI Engineer
A foundational understanding of how to use ML can be an asset to an AI Engineer. This course may help AI Engineers gain the initial knowledge to use ML and incorporate it into their work, potentially increasing their value within the team.
Software Engineer
Software Engineer
For Software Engineers with an interest in specializing in ML-related software, this course may help build a foundation for them to get started. Additionally, the course may help Software Engineers understand how to apply ML in their software development and incorporate ML-related features within their software.
Data Engineer
Data Engineer
Data Engineers can also use ML to improve the architecture of their data systems. This course may help build a basic foundation for a Data Engineer to approach and incorporate ML in data system architecture.
Business Analyst
Business Analyst
Business Analysts may use ML to develop dashboards and presentations for visualizing data. This course will help build a foundation in ML, improving a Business Analyst's ability to analyze data using ML.
Product Manager
Product Manager
Product Managers who understand ML can potentially identify and develop more innovative features for their products. This course will help build a foundation for Product Managers to approach, understand, and use ML within the development of software products.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts may use ML to develop more complex models. This course will help build a foundation for a Quantitative Analyst to approach ML and potentially use it in the development of financial quantitative models.
Statistician
Statistician
Statisticians may use ML to develop more complex statistical models. This course will help build a foundation for a Statistician to approach ML and potentially use it in the development of statistical models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts may use ML to analyze complex data to develop better strategies for a business. This course will help build a foundation for an Operations Research Analyst to approach ML and potentially use it in the analysis of complex data for strategy development.
Financial Analyst
Financial Analyst
Financial Analysts may use ML to analyze financial data and identify trends. This course will help build a foundation for a Financial Analyst to approach ML and potentially use it in financial analysis.
Actuary
Actuary
Actuaries may use ML to analyze data and develop models. This course will help build a foundation for an Actuary to approach ML and potentially use it in the analysis of data and model development.
Economist
Economist
Economists may use ML to analyze economic data. This course will help build a foundation for an Economist to approach ML and potentially use it in the analysis of economic data.
Market Researcher
Market Researcher
Market Researchers may use ML to analyze market data. This course will help build a foundation for a Market Researcher to approach ML and potentially use it in the analysis of market data.
For more career information including salaries, visit:
OpenCourser.com/course/00dd8l/smart
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
Smart Analytics, Machine Learning, and AI on Google Cloud.
Provides a comprehensive overview of machine learning concepts and techniques in Python, serving as a solid foundation for the course's exploration of smart analytics and machine learning.
Provides hands-on experience with machine learning libraries and frameworks, complementing the theoretical concepts covered in the course.
Provides a comprehensive overview of Google Cloud Platform (GCP), including its machine learning services, offering a broader context for the course's focus on smart analytics and machine learning on GCP.
Provides insights into big data analytics using Apache Spark, complementing the course's coverage of big data analytics with Notebooks.
Offers a concise introduction to machine learning algorithms, providing a good starting point for those new to the field and complementing the course's theoretical foundations.
Offers a visual introduction to deep learning concepts, providing a valuable resource for those new to the field and complementing the course's exploration of deep learning models.
Save
Explores the broader implications and future of AI, providing a thought-provoking supplement to the course's focus on the technical aspects of smart analytics and machine learning.
Save
Explores the human side of AI, examining the impact of smart analytics and machine learning on the workforce and the future of work, providing a broader perspective beyond the technical aspects.
Save
Provides a business perspective on the impact of AI, offering insights into how organizations can leverage smart analytics and machine learning to gain a competitive edge.
While this book focuses on Microsoft Azure rather than Google Cloud, it provides valuable insights into real-world machine learning applications and best practices, which can be applied to the course's content.
For those interested in delving deeper into deep learning, this book offers a comprehensive guide to using the Keras library with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/00dd8l/smart
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Rating
4.6
Effort
Approx. 6 hours
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
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