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!
Skills you’ll Learn
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.