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Kuldeep Singh Sidhu
With the amount of available online content ever-increasing and all the platforms trying to grab your attention by giving you personalized recommendations, recommendation engines are more important than ever.
In this project-based course, you will create a...
Read more
With the amount of available online content ever-increasing and all the platforms trying to grab your attention by giving you personalized recommendations, recommendation engines are more important than ever.
In this project-based course, you will create a...
Read more
With the amount of available online content ever-increasing and all the platforms trying to grab your attention by giving you personalized recommendations, recommendation engines are more important than ever.
In this project-based course, you will create a recommendation system using Collaborative Filtering with help of Scikit-surprise library, which learns from past user behavior. We will be working with a movie lense dataset and by the end of this project, you will be able to give unique movie recommendations for every user based on their past ratings.
This project is best suited for anyone who is venturing into data science and is curious as to how recommendation engines work. This project will be a great addition to your portfolio to showcase your real-world hands-on experience with recommendation systems as we would be working with a real-world dataset.
Register for this course and see more details by visiting:
OpenCourser.com/course/ppej5q/movie
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This project is an ideal learning opportunity for aspiring data scientists who are eager to grasp the fundamentals of recommendation systems and enhance their portfolio with real-world experience
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Reviews summary
Movie recommendation project with hands-on
This course is a project-based introduction to building a movie recommendation system using a real-world dataset and the Scikit-surprise library. Reviews for this course are overall positive, with many users praising the hands-on experience and clarity of instruction.
Suited for beginners
"...This project is best suited for anyone who is venturing into data science and is curious as to how recommendation engines work..."
Well-explained
"...Nice introduction to surprise library..."
"...by the end of this project, you will be able to give unique movie recommendations for every user based on their past ratings..."
Real-world project
"...a great addition to your portfolio to showcase your real-world hands-on experience with recommendation systems..."
Needs more practice
"...Off-line Practice is need for lesser equipped users..."
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 Movie Recommendation System using Collaborative Filtering with these
activities:
Review Scikit-learn
Show steps
Refreshes knowledge of Scikit-learn concepts, which will be used throughout the course with Collaborative Filtering.
Browse courses on
scikit-learn
Show steps
-
Revisit documentation and tutorials on the Scikit-learn website.
-
Complete coding exercises to practice using Scikit-learn functions.
Watch Scikit-surprise Tutorials
Show steps
Complements course material with expert guidance on using and applying Scikit-surprise.
Show steps
-
Follow tutorials on the Scikit-surprise documentation.
-
Explore additional resources and tutorials online.
Review Python
Show steps
Brush up on the basics of Python syntax and programming constructs to strengthen your footing before the course begins.
Browse courses on
Python
Show steps
-
Review Python data types, variables, and operators
-
Practice writing simple Python functions
-
Solve beginner-level Python coding problems
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Gather Resources on Recommendation Systems
Show steps
Compiles useful resources, articles, and code snippets for deeper exploration of recommendation systems.
Show steps
-
Search and collect relevant articles and tutorials.
-
Organize resources into a curated collection.
Collaborative Filtering Exercises
Show steps
Reinforces understanding of Collaborative Filtering algorithms and their implementation.
Show steps
-
Solve practice problems on Collaborative Filtering.
-
Implement Collaborative Filtering algorithms from scratch.
Join a study group or online forum
Show steps
Connect with fellow learners to discuss concepts, share resources, and get support throughout the course.
Browse courses on
Recommendation Systems
Show steps
-
Find a study group or online forum dedicated to recommendation systems
-
Participate in discussions, ask questions, and share your insights
-
Collaborate on projects or assignments
Follow tutorials on collaborative filtering
Show steps
Explore tutorials and articles to gain a deeper understanding of collaborative filtering techniques and their applications.
Browse courses on
Collaborative Filtering
Show steps
-
Find tutorials on collaborative filtering algorithms
-
Follow the steps to implement a collaborative filtering system
-
Experiment with different parameters and datasets
Explore Scikit-Surprise User Guide
Show steps
Learn about the basics of Scikit-Surprise library and how to use it for building recommendation systems, which will be helpful for understanding the course concepts.
Show steps
-
Navigate to the Scikit-Surprise User Guide
-
Review the documentation for the main modules and classes
-
Try out some basic examples to get familiar with the API
Create a Recommendation System
Show steps
Develops a practical understanding of building a recommendation system using Collaborative Filtering and Scikit-surprise.
Show steps
-
Gather user and rating data.
-
Prepare and preprocess the data.
-
Train a Collaborative Filtering model using Scikit-surprise.
-
Test and evaluate the performance of the model.
Practice Collaborative Filtering with Scikit-surprise
Show steps
Reinforce your understanding of Collaborative Filtering by implementing it using the Scikit-surprise library.
Browse courses on
Collaborative Filtering
Show steps
-
Install Scikit-surprise and load the movie lens dataset.
-
Train a Collaborative Filtering model.
-
Evaluate the performance of your model.
Build a simple movie recommendation system
Show steps
Apply your skills to create a functional movie recommendation system that can generate personalized recommendations based on user ratings.
Browse courses on
Recommendation Systems
Show steps
-
Gather a dataset of movie ratings
-
Select and implement a collaborative filtering algorithm
-
Develop a user interface for the recommendation system
-
Evaluate the system's performance and make improvements
Contribute to open-source recommendation system projects
Show steps
Get involved in the open-source community by contributing to real-world recommendation system projects, gaining practical experience and enhancing your portfolio.
Browse courses on
Collaborative Filtering
Show steps
-
Find open-source recommendation system projects on platforms like GitHub
-
Identify areas where you can contribute your skills
-
Make code contributions, report bugs, or participate in discussions
Review Scikit-learn
Show steps
Refreshes knowledge of Scikit-learn concepts, which will be used throughout the course with Collaborative Filtering.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Revisit documentation and tutorials on the Scikit-learn website.
- Complete coding exercises to practice using Scikit-learn functions.
Watch Scikit-surprise Tutorials
Show steps
Complements course material with expert guidance on using and applying Scikit-surprise.
Show steps
Show steps
Show steps
- Follow tutorials on the Scikit-surprise documentation.
- Explore additional resources and tutorials online.
Review Python
Show steps
Brush up on the basics of Python syntax and programming constructs to strengthen your footing before the course begins.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python data types, variables, and operators
- Practice writing simple Python functions
- Solve beginner-level Python coding problems
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Gather Resources on Recommendation Systems
Show steps
Compiles useful resources, articles, and code snippets for deeper exploration of recommendation systems.
Show steps
Show steps
Show steps
- Search and collect relevant articles and tutorials.
- Organize resources into a curated collection.
Collaborative Filtering Exercises
Show steps
Reinforces understanding of Collaborative Filtering algorithms and their implementation.
Show steps
Show steps
Show steps
- Solve practice problems on Collaborative Filtering.
- Implement Collaborative Filtering algorithms from scratch.
Join a study group or online forum
Show steps
Connect with fellow learners to discuss concepts, share resources, and get support throughout the course.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Find a study group or online forum dedicated to recommendation systems
- Participate in discussions, ask questions, and share your insights
- Collaborate on projects or assignments
Follow tutorials on collaborative filtering
Show steps
Explore tutorials and articles to gain a deeper understanding of collaborative filtering techniques and their applications.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Find tutorials on collaborative filtering algorithms
- Follow the steps to implement a collaborative filtering system
- Experiment with different parameters and datasets
Explore Scikit-Surprise User Guide
Show steps
Learn about the basics of Scikit-Surprise library and how to use it for building recommendation systems, which will be helpful for understanding the course concepts.
Show steps
Show steps
Show steps
- Navigate to the Scikit-Surprise User Guide
- Review the documentation for the main modules and classes
- Try out some basic examples to get familiar with the API
Create a Recommendation System
Show steps
Develops a practical understanding of building a recommendation system using Collaborative Filtering and Scikit-surprise.
Show steps
Show steps
Show steps
- Gather user and rating data.
- Prepare and preprocess the data.
- Train a Collaborative Filtering model using Scikit-surprise.
- Test and evaluate the performance of the model.
Practice Collaborative Filtering with Scikit-surprise
Show steps
Reinforce your understanding of Collaborative Filtering by implementing it using the Scikit-surprise library.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Install Scikit-surprise and load the movie lens dataset.
- Train a Collaborative Filtering model.
- Evaluate the performance of your model.
Build a simple movie recommendation system
Show steps
Apply your skills to create a functional movie recommendation system that can generate personalized recommendations based on user ratings.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Gather a dataset of movie ratings
- Select and implement a collaborative filtering algorithm
- Develop a user interface for the recommendation system
- Evaluate the system's performance and make improvements
Contribute to open-source recommendation system projects
Show steps
Get involved in the open-source community by contributing to real-world recommendation system projects, gaining practical experience and enhancing your portfolio.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Find open-source recommendation system projects on platforms like GitHub
- Identify areas where you can contribute your skills
- Make code contributions, report bugs, or participate in discussions
Career center
Learners who complete Movie Recommendation System using Collaborative Filtering will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, deploy, and maintain machine learning algorithms and recommendation engines to identify patterns and trends in large datasets. This course can help you build a foundation in collaborative filtering and recommendation systems, which are essential components of many data science projects. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be useful in various industries, including media and retail.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models and systems. This course can help you build a foundation in collaborative filtering and recommendation systems, which are widely used in machine learning applications. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be beneficial in various industries, including finance and healthcare.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in software applications. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various industries, including e-commerce and social media.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns and trends. This course can help you build a foundation in collaborative filtering and recommendation systems, which are used in data analysis to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be useful in various industries, including marketing and customer service.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to drive business decisions. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in business intelligence to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various industries, including sales and marketing.
Product Manager
Product Manager
Product Managers design, develop, and launch products. This course can help you build a foundation in collaborative filtering and recommendation systems, which are used in product management to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be beneficial in various industries, including technology and retail.
Marketer
Marketer
Marketers plan, execute, and analyze marketing campaigns. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in marketing to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various industries, including digital marketing and advertising.
Salesperson
Salesperson
Salespeople sell products and services. This course can help you build a foundation in collaborative filtering and recommendation systems, which are used in sales to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be useful in various industries, including sales and customer service.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve their goals with a product or service. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in customer success to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various industries, including SaaS and technology.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces. This course can help you build a foundation in collaborative filtering and recommendation systems, which are used in user experience design to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be beneficial in various industries, including e-commerce and social media.
Content Curator
Content Curator
Content Curators select, organize, and present content to audiences. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in content curation to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various industries, including publishing and media.
Librarian
Librarian
Librarians help people find and access information. This course can help you build a foundation in collaborative filtering and recommendation systems, which are used in libraries to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be useful in various library settings, including public libraries and academic libraries.
Teacher
Teacher
Teachers educate students in various subjects. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in education to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various educational settings, including schools and universities.
Researcher
Researcher
Researchers conduct research in various fields. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in research to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various research settings, including academia and industry.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. This course can help you build a foundation in collaborative filtering and recommendation systems, which are increasingly used in consulting to make personalized recommendations. You will learn how to use the Scikit-surprise library to create and evaluate recommendation systems, which can be valuable in various consulting settings, including management consulting and technology consulting.
For more career information including salaries, visit:
OpenCourser.com/course/ppej5q/movie
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
Movie Recommendation System using Collaborative Filtering.
Save
Provides a machine learning perspective on recommender systems. It covers different types of recommender systems, evaluation methods, and applications.
A practical guide to applying deep learning techniques to recommender systems, with a focus on real-world applications.
Provides a broad overview of recommender systems. It covers different types of recommender systems, evaluation methods, and applications.
Save
Provides an introduction to recommender systems, including the underlying algorithms, techniques, and applications. It valuable resource for researchers, practitioners, and students who want to learn about recommender systems.
This textbook provides a comprehensive overview of machine learning from a probabilistic perspective. It covers topics such as supervised learning, unsupervised learning, and graphical models.
Provides a comprehensive introduction to information retrieval, covering topics such as text representation, indexing, and retrieval models. Useful for understanding the fundamentals of recommender systems.
This textbook provides a comprehensive overview of the mathematical foundations of machine learning. It covers topics such as linear algebra, calculus, and probability theory.
Provides a comprehensive overview of natural language processing techniques. It covers topics such as tokenization, stemming, and machine translation.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ppej5q/movie
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Instructor
Kuldeep Singh Sidhu
Good to know
This project is an ideal learning opportunity for aspiring data scientists who are eager to grasp the fundamentals of recommendation systems and enhance their portfolio with real-world experience
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