Recommender Systems with Machine Learning

Preview this course

The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.

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76 on-demand videos & exercises
Level: Beginner
6hrs 17mins

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What to know about this course

Have you ever thought how YouTube adjusts your feed as per your favorite content? Ever wondered! Why is your Netflix recommending your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself? Then this is the course you are looking for.

We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems. Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you.

By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models. All the resource files are added to the GitHub repository at:

Who's this course for?

No prior knowledge of recommender systems, machine learning, data analysis, or mathematics is needed. Only the working knowledge of basics of Python is required.

You will start from the basics and gradually build your knowledge in the subject. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs.

The course is suitable for individuals who want to advance their skills in ML, master the relation of data analysis with ML, build customized recommender systems for their applications, and implement ML algorithms for recommender systems.

What you'll learn

  • Explore AI-integrated recommender systems basics
  • Look at the basic taxonomy of recommender systems
  • Study the impact of overfitting, underfitting, bias, and variance
  • Build content-based recommender systems with ML and Python
  • Build item-based recommender systems using ML techniques and Python
  • Learn to model KNN-based recommender engine for applications

Key Features

  • Build recommender systems using ML from the perspective of content-based and collaborative filtering
  • Implementation of ML with data analysis on real-world datasets of movies and Spotify songs
  • Learn to program with Python and how to use ML concepts to develop recommender systems

Course Curriculum

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
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