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Python Deep Learning Recommendation Algorithms

Python Deep Learning Recommendation Algorithms
Python Deep Learning Recommendation Algorithms

Python Deep Learning Recommendation Algorithms

Python, deep learning, and collaborative filtering for machine learning recommendation systems.

What you’ll learn

Python Deep Learning Recommendation Algorithms

  • Create a Python testing and evaluation framework for recommendation systems.
  • Recognize answers to typical problems using extensive recommendation systems.
  • Make suggestions at scale with deep learning.
  • Use the appropriate metrics to assess a suggested system’s performance.


  • Some familiarity with a scripting or programming language (preferably Python) is preferred.
  • It helps to know a little bit about computer science and be able to understand how new algorithms work.


We’ll start with tried-and-true recommendation algorithms built on neighborhood-based collaborative filtering before moving on to more cutting-edge approaches like matrix factorization and deep learning using artificial neural networks. You’ll learn about the problems you might run into when using these algorithms on a large scale and be able to use real-world data based on our vast experience in the field.

You’ve probably seen automatic suggestions all over the place—on the Netflix home page, YouTube, and Amazon—as these machine learning algorithms discover your distinct tastes and provide you with the most relevant goods or entertainment. Understanding how these technologies function will make you very useful to the biggest and most prominent IT organizations out there.

Beginning with tried-and-true algorithms for recommendations like neighborhood-based collaborative filtering, we’ll next go on to more advanced strategies like matrix factorization and even deep learning using artificial neural networks.

Don’t enroll in this course hoping to learn how to write since recommendation systems are complicated. Making a recommendation system doesn’t have a set formula; instead, it requires knowledge of the many algorithms and knowing when to use each one in a certain context. We presume you are already familiar with coding.

Python Deep Learning Recommendation Algorithms

But this course puts a lot of emphasis on learning by doing. You’ll build a framework for comparing and combining different recommendation algorithms, and you’ll even build neural networks with Tensorflow to make suggestions based on real people’s reviews of real movies.

This in-depth course will take you from the first versions of collaborative filtering to the most cutting-edge uses of deep neural networks and modern machine-learning methods for recommending the best products for each user.

If you’re unfamiliar with Python, we provide an introduction, but to utilize this course effectively, you must have some previous programming expertise. In addition, we provide a brief overview of deep learning for those of you who are new to the topic of artificial intelligence. You will nonetheless need to be able to comprehend fresh computer algorithms.

Who this course is for:

  • For those who wish to create software that uses machine learning and deep learning to provide product or content suggestions.
  • For those who work for or are interested in working for major websites or e-commerce businesses.
  • Researchers in computer science


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