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Movie Recommendation System using Collaborative Filtering
Kuldeep Singh Sidhu
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
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
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
Coming soon
We're preparing activities for
Movie Recommendation System using Collaborative Filtering.
These are activities you can do either before, during, or after a course.
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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Coursera
Institution
Coursera Project Network
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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