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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
Content-Based Recommendation Systems
This module demonstrates how to build a recommendation system using characteristics of the users and items and how to use Qwiklabs to complete each of your labs using Google Cloud.
Collaborative Filtering Recommendations Systems
This module shows how the data of the interactions between users and items from many different users can be combined to improve the quality of predictions.
Neural Networks for Recommendation Systems
This module shows how various recommendation systems can be combined as part of a hybrid approach.
Reinforcement Learning
This module presents the goals of reinforcement learning and shows where reinforcement learning fits in machine learning.
Summary
This module reviews the topics explored in this course.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Recommendation Systems on Google Cloud to your list so you can find it easily later:
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Reviews summary
Google cloud recommendation systems
Learners say that this course is an informative and **very useful** introduction to applying **practical** Recommendation Systems on Google Cloud Platform. Hands-on labs and insightful discussions help students implement collaborative filtering, content-based filtering, TensorFlow, and more to build cutting-edge systems. However, learners also note that some of the labs contain outdated content, require bug fixes, or are mismatched with the video content.
The labs are **hands-on** and **engaging**.
"An awesome course. Excellent explanation of concepts as well as programs.Easy lab setups and hands on learning."
"Labs illustrate very well the concepts and clarify the practical issues and solutions with gcp & tf. Excellent teaching !"
"The lab videos are some of the most in depth in this specialization."
The course provides **great**, **informative** content.
"This is a wonderful course, to learn about the practical implementation of recommendation systems on Google Cloud Platform."
"Kudos to team gcp, practical guide to implementing a recommendation system and helpful overview of gcp tml ools"
"It is a very useful. Recomended."
The content for this course is **outdated**.
"The content is out of date in applications."
"Don't enroll in this course because the qwiklabs and the videos are completely different the qwiklabs used bq while the videos taught tf"
" My first one-star course on coursera which is especially disappointing given that it is a course from google."
Some labs contain **outdated content** or **bugs**.
"The Qwiklabs materials are outdated, however the lectures are insightful."
"Composer component takes too long to initiate. Almost more than 25 minutes. Please fix it."
"Some labs with bigquery and the movieLens are not working."
"The final lab of the course (and also the specialization) has been unavailable for a few days now."
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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Effort
2 weeks of study, 6 - 8 hours per week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
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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