Building Recommendation Systems
Enroll in our free Building Recommendation Systems course and learn the fundamentals of Machine Learning, algorithms, and techniques used in creating effective recommendation systems. Best for Beginners. Enroll for free now!
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About this course
Many individuals want to learn how to build a recommender system in Python. Recommendation systems are one of the most widely used applications of Machine Learning. For example, if you have ever visited the site Amazon, you will get recommendations of similar products that you wish to buy. Well, this is a perfect example of a recommendation system. Similarly, if you are on Netflix, you would get similar recommendations to the movies or television shows that you generally watch. Understanding the importance of Recommendation systems, we have developed this course on Building Recommendation Systems with Python. In this course, we shall start with an introduction to recommendation systems and look at the types of recommendation systems. We shall have a comprehensive demo on building a Movie Recommendation system.
The world’s top universities, like the UT Austin and SRM Institute of Science and Technology, have collaborated with Great Learning to design the industry’s best Postgraduate and Degree programs in AIML. They offer world-class AIML Courses in the current market, which provides learners with a first-class education from the highly experienced faculty. Our objective is to guide learners to become successful AIML professionals.
Course Outline
Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts.
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Frequently Asked Questions
Will I get a certificate after completing this Building Recommendation Systems free course?
Yes, you will get a certificate of completion for Building Recommendation Systems after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this Building Recommendation Systems course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Building Recommendation Systems can get started with this course.
Is there any limit on how many times I can take this free course?
Once you enroll in the Building Recommendation Systems course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free Building Recommendation Systems course?
Great Learning Academy provides this Building Recommendation Systems course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
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Building Recommendation Systems: An Overview
Algorithm-based technology utilizes recommendation systems to predict a user's potential preference or interest in a particular item. Various industries, such as e-commerce, streaming, and social media, widely use this technology. These systems are designed to provide personalized and relevant recommendations to users based on their interests, tastes, and preferences.
Users commonly receive recommendations for products and services, content such as videos, articles, and music, and even potential friends and romantic partners. Amazon's "Customers Who Bought This Item Also Bought" and Netflix's "Suggestions for You" are examples of popular recommendation systems.
Building recommendation systems in Machine Learning requires an understanding of various algorithms and techniques. The most popular algorithms used in recommendation systems include content-based filtering, collaborative filtering, and hybrid recommendation models. Content-based filtering utilizes user data, such as ratings and reviews, to recommend similar items. Collaborative filtering uses the data from other users to determine what items may interest a particular user. Hybrid models combine the two methods to provide more accurate and personalized recommendations.
Sophisticated recommendation systems can also be created using Machine Learning. Machine learning techniques can be used to analyze user behavior to identify patterns and trends that are used to generate more accurate and personalized recommendations.
Great Learning provides a free course on building recommendation systems using Machine Learning. This course covers the fundamentals of Machine Learning, including the principles of recommendation systems, as well as practical techniques for building effective recommendation systems. Students gain experience using popular Machine Learning algorithms and techniques to create powerful recommendation systems. The course also covers topics such as data pre-processing, feature engineering, and model evaluation. Upon completion of the course, students acquire the skills and knowledge required to build and implement effective recommendation systems.