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Charles Ivan Niswander II
In this 2-hour long project-based course, you will learn how to build a Recommender System in Python.
Youtube, Amazon, Google, Netflix…. all of these well-known services are known for their 'magic' algorithms that uncannily predict what videos or movies we...
Read more
In this 2-hour long project-based course, you will learn how to build a Recommender System in Python.
Youtube, Amazon, Google, Netflix…. all of these well-known services are known for their 'magic' algorithms that uncannily predict what videos or movies we...
Read more
In this 2-hour long project-based course, you will learn how to build a Recommender System in Python.
Youtube, Amazon, Google, Netflix…. all of these well-known services are known for their 'magic' algorithms that uncannily predict what videos or movies we would enjoy or what products we might be interested in buying. But how do these recommender systems work? Fortunately, they are simple enough to be understood by the average Python programmer. By the time you've finished "Build a Recommender System in Python", you'll have coded by hand 4 different types of recommender systems that mimic the techniques of Amazon, Netflix, and YouTube.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/u3sya4/build
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possible dealbreakers
Introduces learners to building recommender systems using Python, a common task in industry
Charles Ivan Niswander II is a recognized instructor in the field of recommender systems
Though the course is intended for beginners, it still requires prior knowledge of Python
This course is best suited for learners in the North America region, as the course content may not be as relevant to learners in other regions
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Save Build a Recommender System in Python to your list so you can find it easily later:
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Reviews summary
Skewed opinions
The experiences for students have been limited, with some students satisfied and others extremely dissatisfied. For the most part, students complain about the technical aspects, course depth, and price. If you are interested in learning more about this course, we recommend that you speak to other students.
Instructor explains methods well.
"Best course on recommenders I've come across."
"Instructor simply copy/pastes the code with minimum to no explanation."
Course value is low for some.
"This course is one of the worst courses I have taken, ever."
"The total content is around 30 mins or so and very superficial."
Stuggling with technical difficulties.
"None of the dataset files were downloaded.....I don know what was the reason."
"very limited explanation, the interactive coding tool doesn't work at all."
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 Build a Recommender System in Python with these
activities:
Review Python programming basics
Show steps
Review the foundational concepts of Python programming to ensure you have a solid understanding before starting this course.
Browse courses on
Python Basics
Show steps
-
Go over Python data types, variables, and operators.
-
Practice writing simple Python programs.
Join a study group or online forum
Show steps
Connect with other students or professionals interested in recommender systems to discuss the course materials and share ideas.
Show steps
-
Find a study group or online forum related to recommender systems.
-
Participate in discussions and ask questions.
Solve coding challenges
Show steps
Practice your Python skills by solving coding challenges on websites like LeetCode or HackerRank.
Show steps
-
Choose a coding challenge that is relevant to recommender systems.
-
Solve the challenge using Python.
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a simple recommender system
Show steps
Create a simple recommender system from scratch to apply the concepts you learn in this course.
Browse courses on
Python
Show steps
-
Choose a dataset to use for your recommender system.
-
Implement a collaborative filtering algorithm.
-
Evaluate the performance of your recommender system.
Write a blog post or article about recommender systems
Show steps
Summarize the concepts you learn in this course by writing a blog post or article about recommender systems.
Browse courses on
Python
Show steps
-
Choose a topic for your blog post or article.
-
Research and write about the topic.
-
Publish your blog post or article.
Contribute to an open-source recommender system project
Show steps
Gain practical experience and contribute to the open-source community by working on a real-world recommender system project.
Browse courses on
Open Source
Show steps
-
Find an open-source recommender system project.
-
Identify an area where you can contribute.
-
Submit a pull request with your contribution.
Review Python programming basics
Show steps
Review the foundational concepts of Python programming to ensure you have a solid understanding before starting this course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go over Python data types, variables, and operators.
- Practice writing simple Python programs.
Join a study group or online forum
Show steps
Connect with other students or professionals interested in recommender systems to discuss the course materials and share ideas.
Show steps
Show steps
Show steps
- Find a study group or online forum related to recommender systems.
- Participate in discussions and ask questions.
Solve coding challenges
Show steps
Practice your Python skills by solving coding challenges on websites like LeetCode or HackerRank.
Show steps
Show steps
Show steps
- Choose a coding challenge that is relevant to recommender systems.
- Solve the challenge using Python.
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a simple recommender system
Show steps
Create a simple recommender system from scratch to apply the concepts you learn in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a dataset to use for your recommender system.
- Implement a collaborative filtering algorithm.
- Evaluate the performance of your recommender system.
Write a blog post or article about recommender systems
Show steps
Summarize the concepts you learn in this course by writing a blog post or article about recommender systems.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a topic for your blog post or article.
- Research and write about the topic.
- Publish your blog post or article.
Contribute to an open-source recommender system project
Show steps
Gain practical experience and contribute to the open-source community by working on a real-world recommender system project.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source recommender system project.
- Identify an area where you can contribute.
- Submit a pull request with your contribution.
Career center
Learners who complete Build a Recommender System in Python will develop knowledge and skills
that may be useful to these careers:
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business data and making recommendations to improve business outcomes. The "Build a Recommender System in Python" course can be especially useful for Business Analysts who want to learn how to use data analysis to identify opportunities for improvement and develop strategies to address them. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Business Analysts.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems and make better decisions. The "Build a Recommender System in Python" course can be especially useful for Data Analysts who want to learn how to use data to build recommender systems. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use data to solve complex business problems and make better decisions. The "Build a Recommender System in Python" course can be especially useful for Data Scientists who want to learn how to use data to build recommender systems. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. The "Build a Recommender System in Python" course can be especially useful for Machine Learning Engineers who want to learn how to build recommender systems. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Machine Learning Engineers.
Researcher
Researcher
Researchers use data to answer questions about the world around us. The "Build a Recommender System in Python" course can be especially useful for Researchers who want to learn how to use data to conduct research. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Researchers.
Statistician
Statistician
Statisticians use data to make inferences about the world around us. The "Build a Recommender System in Python" course can be especially useful for Statisticians who want to learn how to use data to build recommender systems. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Statisticians.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The "Build a Recommender System in Python" course can be especially useful for Software Engineers who want to learn how to build recommender systems. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Software Engineers.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. The "Build a Recommender System in Python" course can be especially useful for Product Managers who want to learn how to use data to make better decisions about product development. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Product Managers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. The "Build a Recommender System in Python" course can be especially useful for Quantitative Analysts who want to learn how to use data to predict the performance of investments. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Quantitative Analysts.
UX Designer
UX Designer
UX Designers design user interfaces for websites and mobile applications. The "Build a Recommender System in Python" course can be especially useful for UX Designers who want to learn how to use data to improve the user experience. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for UX Designers.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. The "Build a Recommender System in Python" course can be especially useful for Web Developers who want to learn how to use data to improve the performance of their websites. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Web Developers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for planning and executing marketing campaigns. The "Build a Recommender System in Python" course can be especially useful for Marketing Managers who want to learn how to use data to improve the effectiveness of their marketing campaigns. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Marketing Managers.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. The "Build a Recommender System in Python" course can be especially useful for Customer Success Managers who want to learn how to use data to improve the customer experience. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Customer Success Managers.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. The "Build a Recommender System in Python" course can be especially useful for Sales Managers who want to learn how to use data to improve the performance of their sales teams. The course covers topics such as data collection, data analysis, and statistical modeling, which are all essential skills for Sales Managers.
For more career information including salaries, visit:
OpenCourser.com/course/u3sya4/build
Reading list
We've selected eight 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
Build a Recommender System in Python.
This textbook provides a comprehensive overview of recommender systems. It would be useful as a reference tool for more advanced students.
Provides a practical guide to building recommender systems. It would be useful as a companion to this course.
Provides a comprehensive overview of machine learning from a probabilistic perspective, which is important in recommender systems. It would be useful as a reference tool for more advanced students with a strong technical background.
Provides a detailed overview of matrix factorization techniques used in building recommender systems. It would be useful as a reference tool for more advanced students.
Provides a comprehensive overview of Bayesian reasoning and machine learning, which are important concepts in recommender systems. It would be useful as a reference tool for more advanced students with a strong technical background.
Provides a solid foundation in the mathematics used in machine learning and recommender systems. It would be useful as a background reference for students with a strong technical background.
Provides supplemental material on information retrieval concepts which are used in building recommender systems. It would be useful as a background reference early in the course.
Save
Provides supplemental material on deep learning concepts which are used in building recommender systems. It would be useful as a background reference later in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u3sya4/build
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Introduces learners to building recommender systems using Python, a common task in industry
Charles Ivan Niswander II is a recognized instructor in the field of recommender systems
Though the course is intended for beginners, it still requires prior knowledge of Python
This course is best suited for learners in the North America region, as the course content may not be as relevant to learners in other regions
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