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Google Cloud Training
In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
Register for this course and see more details by visiting:
OpenCourser.com/course/yjtlro/recommendation
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What's inside
Syllabus
Welcome to Recommendation Systems on Google Cloud
This module previews the topics covered in the course.
Recommendation Systems Overview
This module defines what recommendation systems are, reviews the different types of recommendation systems, and discusses common problems that arise when developing recommendation systems.
Read more
Syllabus
Welcome to Recommendation Systems on Google Cloud
This module previews the topics covered in the course.
Recommendation Systems Overview
This module defines what recommendation systems are, reviews the different types of recommendation systems, and discusses common problems that arise when developing recommendation systems.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a comprehensive study of recommendation systems using machine learning techniques
Leverages Google Cloud Platform for hands-on labs, providing practical experience in building and deploying recommendation systems
Introduces reinforcement learning and explains its role in recommendation systems
Incorporates collaborative filtering, content-based filtering, and neural networks for a comprehensive understanding of recommendation system approaches
Suitable for individuals with prior knowledge of machine learning and data analysis
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Reviews summary
Practical recsys on google cloud
According to learners, this course provides a solid introduction to building recommendation systems, specifically focusing on implementation within the Google Cloud ecosystem. Many highlight the hands-on labs as the most valuable part, offering practical experience with tools like Vertex AI and BigQuery ML, making the concepts feel applicable to real-world problems. However, a few students mention that the course assumes prior knowledge in both machine learning and Google Cloud, which might make it challenging for absolute beginners. Some also noted that certain theoretical explanations could use more depth, and the module on Reinforcement Learning felt somewhat separate.
Good intro, but some topics lack depth.
"A good overview, but some of the more complex theoretical parts could have been explored deeper."
"The Reinforcement Learning module felt a bit disjointed from the rest of the course."
"Covers a wide range of RS types but doesn't go into extreme detail on any single one."
"Provides a solid foundation to build upon with further study."
Requires prior ML and GCP experience.
"This course assumes a good understanding of machine learning fundamentals before diving in."
"You really need to be familiar with Google Cloud services before taking this."
"Not recommended if you are completely new to either ML or GCP; it moves quite fast."
"Would benefit from a clearer outline of necessary prerequisites."
Applicable to building production systems.
"The content covered is highly relevant for anyone looking to build recommendation engines in a production environment."
"I can see how these techniques can be applied directly to my work projects."
"Provides a practical framework for implementing RS solutions using modern cloud technology."
"Focused on practical application rather than just abstract theory."
Provides valuable practical experience on GCP.
"The hands-on labs using Vertex AI and BigQuery ML are incredibly useful for understanding how to implement these systems on GCP."
"I loved the practical exercises; they really helped cement the theoretical concepts."
"Working directly with the tools in Qwiklabs was the highlight of the course for me."
"The labs are well-structured and guide you step-by-step through the implementation process on Google Cloud."
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 Recommendation Systems on Google Cloud with these
activities:
Explore the Google Cloud AI Platform
Show steps
Familiarize yourself with the tools and services available on Google Cloud AI Platform for building and deploying machine learning models
Browse courses on
Google Cloud AI Platform
Show steps
-
Create a Google Cloud account
-
Explore the Google Cloud AI Platform console
-
Follow tutorials on deploying machine learning models to Google Cloud
Review Matrix Factorization
Show steps
Review the concept of using matrix factorization to extract relevant patterns from data
Browse courses on
Matrix Factorization
Show steps
-
Review the mathematical underpinnings of matrix factorization
-
Go through examples of applying matrix factorization to collaborative filtering
Attend a meetup for machine learning enthusiasts
Show steps
Connect with other machine learning practitioners and learn about the latest trends and advancements in the field
Show steps
-
Search for meetups in your area related to machine learning
-
Register for a meetup and attend
-
Network with other attendees and share knowledge
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a recommender system for movie recommendations
Show steps
Apply collaborative filtering techniques to build a recommender system that predicts movie ratings for users
Browse courses on
Collaborative Filtering
Show steps
-
Gather a dataset of movie ratings
-
Preprocess the data and build a user-item rating matrix
-
Implement a collaborative filtering algorithm to make predictions
-
Evaluate the performance of your recommender system
Design and implement a hybrid recommender system
Show steps
Combine multiple recommendation techniques to create a more accurate and personalized recommender system
Browse courses on
Hybrid Recommender Systems
Show steps
-
Research different recommendation techniques
-
Design the architecture of your hybrid recommender system
-
Implement your hybrid recommender system using a programming language
-
Evaluate the performance of your hybrid recommender system
Contribute to an open-source project for recommender systems
Show steps
Gain practical experience and contribute to the machine learning community by participating in an open-source recommender system project
Browse courses on
Open Source
Show steps
-
Identify an open-source recommender system project
-
Review the project's documentation and codebase
-
Identify a feature or bug to work on
-
Submit a pull request with your contribution
Explore the Google Cloud AI Platform
Show steps
Familiarize yourself with the tools and services available on Google Cloud AI Platform for building and deploying machine learning models
Browse courses on
Google Cloud AI Platform
Show steps
Show steps
Show steps
- Create a Google Cloud account
- Explore the Google Cloud AI Platform console
- Follow tutorials on deploying machine learning models to Google Cloud
Review Matrix Factorization
Show steps
Review the concept of using matrix factorization to extract relevant patterns from data
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Review the mathematical underpinnings of matrix factorization
- Go through examples of applying matrix factorization to collaborative filtering
Attend a meetup for machine learning enthusiasts
Show steps
Connect with other machine learning practitioners and learn about the latest trends and advancements in the field
Show steps
Show steps
Show steps
- Search for meetups in your area related to machine learning
- Register for a meetup and attend
- Network with other attendees and share knowledge
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a recommender system for movie recommendations
Show steps
Apply collaborative filtering techniques to build a recommender system that predicts movie ratings for users
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Gather a dataset of movie ratings
- Preprocess the data and build a user-item rating matrix
- Implement a collaborative filtering algorithm to make predictions
- Evaluate the performance of your recommender system
Design and implement a hybrid recommender system
Show steps
Combine multiple recommendation techniques to create a more accurate and personalized recommender system
Browse courses on
Hybrid Recommender Systems
Show steps
Show steps
Show steps
- Research different recommendation techniques
- Design the architecture of your hybrid recommender system
- Implement your hybrid recommender system using a programming language
- Evaluate the performance of your hybrid recommender system
Contribute to an open-source project for recommender systems
Show steps
Gain practical experience and contribute to the machine learning community by participating in an open-source recommender system project
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source recommender system project
- Review the project's documentation and codebase
- Identify a feature or bug to work on
- Submit a pull request with your contribution
Career center
Learners who complete Recommendation Systems on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians are responsible for collecting and analyzing data to draw conclusions about the world around us. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for collecting and analyzing data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models and applications. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for developing and deploying machine learning models and applications.
Research Scientist
Research Scientist
As a Research Scientist, you will be responsible for conducting research in the field of artificial intelligence and machine learning. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for conducting research in this field.
Professor
Professor
Professors are responsible for teaching and conducting research in a variety of academic disciplines. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for teaching and conducting research in this field.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for developing and executing marketing campaigns that are effective and reach the target audience.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and implementing mathematical models to solve business problems. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for developing and implementing mathematical models.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for analyzing financial data and making investment recommendations.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to businesses and organizations. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for identifying and assessing risks.
Consultant
Consultant
Consultants are responsible for providing advice to businesses and organizations on a variety of topics. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for providing advice on how to use machine learning to solve business problems.
Teacher
Teacher
Teachers are responsible for educating students in a variety of subjects. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for teaching students about machine learning.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for analyzing business processes and identifying opportunities for improvement.
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, analyzing, and interpreting data to identify trends and patterns. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for analyzing data and drawing meaningful conclusions.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for analyzing data and drawing meaningful conclusions.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for developing and launching new products that meet the needs of customers.
Software Engineer
Software Engineer
As a Software Engineer, you will design and develop software systems for businesses and organizations. This course may be useful for you because it can help you build a foundation in machine learning algorithms and techniques, which are essential for developing modern software systems.
For more career information including salaries, visit:
OpenCourser.com/course/yjtlro/recommendation
Reading list
We've selected nine 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
Recommendation Systems on Google Cloud.
Focuses on the application of deep learning techniques to recommender systems. It provides a detailed overview of different deep learning architectures and their use cases in recommendation scenarios.
This handbook provides a comprehensive overview of recommender systems, covering various algorithms, techniques, and applications. It valuable reference for both researchers and practitioners in the field.
Save
Provides a comprehensive overview of evaluation techniques for recommender systems. It valuable resource for researchers and practitioners alike.
Introduces machine learning techniques used in building recommender systems. It covers supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical guide to machine learning algorithms and techniques, including Python code examples.
Provides a comprehensive overview of machine learning techniques used in recommender systems. It covers both supervised and unsupervised learning algorithms.
Covers different algorithms and techniques used in recommender systems, including content-based, collaborative filtering, and hybrid approaches. It also discusses the application of recommender systems in various domains.
Provides a practical guide to TensorFlow, a popular open-source machine learning library.
Offers a non-mathematical approach to machine learning, making it accessible to a wider audience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yjtlro/recommendation
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Rating
4.5
Effort
Approx. 13 hours
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
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