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In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
Read more
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
Read more
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
Register for this course and see more details by visiting:
OpenCourser.com/course/ynsqeg/recommendation
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What's inside
Syllabus
Recommendation Systems Overview
Content-Based Recommendation Systems
COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
Neural Networks for Recommendation Systems
Read more
Syllabus
Recommendation Systems Overview
Content-Based Recommendation Systems
COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
Neural Networks for Recommendation Systems
Read more
Building an End-to-End Recommendation System
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
Covers unique perspectives and ideas
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Teaches skills, knowledge, and/or tools that are useful for personal growth and development
Save this course
Save Recommendation Systems with TensorFlow on GCP to your list so you can find it easily later:
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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 with TensorFlow on GCP with these
activities:
Review collaborative filtering techniques
Show steps
The course builds upon collaborative filtering techniques. Reviewing will aid understanding.
Browse courses on
Collaborative Filtering
Show steps
-
Revise key concepts in collaborative filtering
-
Practice solving problems involving collaborative filtering
-
Review case studies and applications of collaborative filtering
Review machine learning basics
Show steps
This course builds upon basic machine learning concepts. Reviewing these concepts will be helpful.
Browse courses on
Machine Learning
Show steps
-
Revisit key concepts in machine learning
-
Solve practice problems in machine learning
-
Review case studies and applications of machine learning
Review the basics of Classification Models
Show steps
Review core concepts to strengthen foundation.
Browse courses on
Classification Models
Show steps
-
Read textbook chapters or online resources
-
Attend an online lecture or workshop
-
Complete practice exercises or quizzes
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice building embeddings
Show steps
Gain proficiency in working with embeddings through repetitive exercises, enhancing understanding and reinforcing skills.
Browse courses on
Embeddings
Show steps
-
Utilize online platforms like Kaggle or LeetCode
-
Participate in coding challenges or competitions
-
Create your own datasets and practice building embeddings
Form study groups
Show steps
Forming a study group can help you retain information better than studying alone.
Show steps
-
Coordinate with peers to create a group
-
Set regular meeting times
-
Establish a supportive environment and communication style
Deep dive into neural networks
Show steps
The course covers neural networks. Completing practice drills will reinforce your understanding.
Browse courses on
Neural Networks
Show steps
-
Solve problems involving neural networks
-
Implement neural networks in code
-
Review the theoretical foundations of neural networks
Attend a workshop on deep learning for recommendation systems
Show steps
Workshops provide hands-on experience. Attending one on deep learning for recommendation systems will be beneficial.
Browse courses on
Recommendation Systems
Show steps
-
Research and identify relevant workshops
-
Register and attend a workshop that aligns with your interests
-
Actively participate in the workshop and ask questions
Explore recommendation systems frameworks
Show steps
The course covers recommendation systems frameworks. Guided tutorials will help you explore them.
Browse courses on
Recommendation Systems
Show steps
-
Use frameworks to build recommendation systems
-
Learn about the strengths and weaknesses of different frameworks
-
Build a recommendation system using a framework of your choice
Build a simple recommendation system using Collaborative Filtering
Show steps
Apply theoretical knowledge to practical implementation, solidifying understanding of Collaborative Filtering techniques.
Browse courses on
Recommendation Systems
Show steps
-
Gather data and preprocess it
-
Choose and implement a Collaborative Filtering algorithm
-
Evaluate the performance of your system
Develop a personalized recommendation engine
Show steps
The course covers building recommendation engines. Creating one will solidify your skills.
Show steps
-
Gather data and preprocess it
-
Choose and implement a recommendation algorithm
-
Evaluate your recommendation engine
-
Deploy your recommendation engine
Contribute to open-source recommendation engine projects
Show steps
The course emphasizes real-world applications. Contributing to open-source projects will provide practical experience.
Show steps
-
Find open-source recommendation engine projects
-
Identify areas where you can contribute
-
Submit your contributions and engage with the community
Review collaborative filtering techniques
Show steps
The course builds upon collaborative filtering techniques. Reviewing will aid understanding.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Revise key concepts in collaborative filtering
- Practice solving problems involving collaborative filtering
- Review case studies and applications of collaborative filtering
Review machine learning basics
Show steps
This course builds upon basic machine learning concepts. Reviewing these concepts will be helpful.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit key concepts in machine learning
- Solve practice problems in machine learning
- Review case studies and applications of machine learning
Review the basics of Classification Models
Show steps
Review core concepts to strengthen foundation.
Browse courses on
Classification Models
Show steps
Show steps
Show steps
- Read textbook chapters or online resources
- Attend an online lecture or workshop
- Complete practice exercises or quizzes
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice building embeddings
Show steps
Gain proficiency in working with embeddings through repetitive exercises, enhancing understanding and reinforcing skills.
Browse courses on
Embeddings
Show steps
Show steps
Show steps
- Utilize online platforms like Kaggle or LeetCode
- Participate in coding challenges or competitions
- Create your own datasets and practice building embeddings
Form study groups
Show steps
Forming a study group can help you retain information better than studying alone.
Show steps
Show steps
Show steps
- Coordinate with peers to create a group
- Set regular meeting times
- Establish a supportive environment and communication style
Deep dive into neural networks
Show steps
The course covers neural networks. Completing practice drills will reinforce your understanding.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Solve problems involving neural networks
- Implement neural networks in code
- Review the theoretical foundations of neural networks
Attend a workshop on deep learning for recommendation systems
Show steps
Workshops provide hands-on experience. Attending one on deep learning for recommendation systems will be beneficial.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend a workshop that aligns with your interests
- Actively participate in the workshop and ask questions
Explore recommendation systems frameworks
Show steps
The course covers recommendation systems frameworks. Guided tutorials will help you explore them.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Use frameworks to build recommendation systems
- Learn about the strengths and weaknesses of different frameworks
- Build a recommendation system using a framework of your choice
Build a simple recommendation system using Collaborative Filtering
Show steps
Apply theoretical knowledge to practical implementation, solidifying understanding of Collaborative Filtering techniques.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Gather data and preprocess it
- Choose and implement a Collaborative Filtering algorithm
- Evaluate the performance of your system
Develop a personalized recommendation engine
Show steps
The course covers building recommendation engines. Creating one will solidify your skills.
Show steps
Show steps
Show steps
- Gather data and preprocess it
- Choose and implement a recommendation algorithm
- Evaluate your recommendation engine
- Deploy your recommendation engine
Contribute to open-source recommendation engine projects
Show steps
The course emphasizes real-world applications. Contributing to open-source projects will provide practical experience.
Show steps
Show steps
Show steps
- Find open-source recommendation engine projects
- Identify areas where you can contribute
- Submit your contributions and engage with the community
Career center
Learners who complete Recommendation Systems with TensorFlow on GCP will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course provides a comprehensive overview of machine learning concepts and techniques, including neural networks and embeddings. By completing this course, you will gain the skills necessary to build and deploy recommendation systems that can help businesses improve customer engagement and satisfaction.
Data Scientist
Data Scientist
A Data Scientist develops, deploys, and maintains statistical and machine learning models to uncover hidden insights in complex data. This course helps build a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to design and implement recommendation systems that can help businesses improve customer engagement and satisfaction.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to develop the backend infrastructure for recommendation systems.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course provides a comprehensive overview of machine learning concepts and techniques, including neural networks and embeddings. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Business Analyst
Business Analyst
A Business Analyst helps organizations to improve their business processes. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the business value of recommendation systems and how they can be used to drive growth.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and implementing marketing campaigns. This course provides a comprehensive overview of machine learning concepts and techniques, including neural networks and embeddings. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk and uncertainty. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Auditor
Auditor
An Auditor examines financial records to ensure accuracy and compliance. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Compliance Analyst
Compliance Analyst
A Compliance Analyst ensures that organizations comply with laws and regulations. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to help organizations make informed decisions. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates the financial performance of companies and makes investment recommendations. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks to businesses. This course provides a foundation in machine learning and neural networks, which are essential for building recommendation systems. By completing this course, you will gain the skills necessary to understand the technical aspects of recommendation systems and how they can be used to improve customer engagement and satisfaction.
For more career information including salaries, visit:
OpenCourser.com/course/ynsqeg/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 with TensorFlow on GCP.
Provides a comprehensive overview of recommender systems, including foundational concepts, techniques, and applications. It valuable resource for understanding the underlying principles and best practices in recommender system design and implementation.
Offers a comprehensive introduction to machine learning techniques and tools, including those used in recommender systems. It valuable resource for those seeking a broader understanding of machine learning concepts and applications.
Provides a comprehensive overview of recommender system algorithms and their applications. It useful reference for those seeking a broad understanding of the field.
Offers a broader perspective on machine learning techniques for recommender systems, covering topics such as natural language processing and deep learning. It valuable reference for those interested in exploring diverse approaches to recommendation.
Provides a foundational understanding of deep learning concepts and techniques. While not specifically focused on recommender systems, it useful resource for those seeking to strengthen their knowledge of deep learning.
Provides a gentle introduction to machine learning concepts and techniques. While not specifically focused on recommender systems, it useful resource for those seeking a foundational understanding of machine learning.
Includes a chapter on recommender systems, providing a foundational understanding of the topic. It valuable resource for those seeking a broader perspective on data mining techniques.
Is primarily focused on natural language processing (NLP) but includes a chapter on using NLP techniques in recommender systems. It useful resource for those interested in exploring the intersection of NLP and recommender systems.
Provides an introduction to the Google Cloud Platform (GCP), which is the platform used in the course. It valuable resource for those unfamiliar with GCP and its services.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ynsqeg/recommendation
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Time
406 hours
Level
Advanced
Via
Pluralsight
Instructor
Google Cloud
Language
English
This course belongs to a
collection
called
Advanced Machine Learning on Google Cloud. It's comprised of five courses:
- End-to-End Machine Learning with TensorFlow on Google Cloud
- Production Machine Learning Systems
- Image Understanding with TensorFlow on GCP
- Sequence Models for Time Series and Natural Language Processing on Google Cloud
- Recommendation Systems with TensorFlow on GCP (viewing)
Good to know
Builds a strong foundation for beginners
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
Covers unique perspectives and ideas
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Teaches skills, knowledge, and/or tools that are useful for personal growth and development
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