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Develop Recommendation Engine with PYTHON 2022

Develop Recommendation Engine with PYTHON 2022
Develop Recommendation Engine with PYTHON 2022

Develop Recommendation Engine with PYTHON 2022

Find out how Amazon, Netflix, Youtube, and IMDB use techniques to help people find what they want.

What you’ll learn

Develop Recommendation Engine with PYTHON 2022

  • Learn how to use Collaborative Filtering Recommendation.
  • Learn how to use Content-Based Filtering Recommendation.
  • You will learn how to build a hybrid recommendation engine.
  • Learn how Amazon and Netflix use these techniques to show people what to buy.
  • Learn the basics about the Recommendation Engine.


  • Python is on the PC.
  • People who know a little bit about python and pandas, and NumPy.


During this class, you’re going to learn about recommendation systems It’s also called a “recommender engine.” Netflix says that 70 percent of the videos that people watch are recommended to them by other people who use the service. Netflix and Amazon also have a lot of products because of their system of recommending them. During this learning path, you’ll mostly learn about techniques that are easy to moderate. You’ll also get to try them out.

People use this system to give each other ideas.

Recommender systems try to figure out what people like and then show them products that might be interesting to them. In order to make more money, online retailers use some of the most powerful machine learning systems to help them make more money. Users give movies and songs a rating when they’ve seen them, search engines and purchase histories, or other information about them or the items they like.

Collaborative filtering and Recommendation systems and content-based filtering and Recommendation systems are two types of Recommendation systems, and they both work. After you finish the class, you’ll be very good at both methods. This isn’t the only thing you’ll learn about. You’ll also learn more about cosine, Pearson correlation, and machine learning algorithms like Logistic regression and K-nearest to get the best recommendation.

Who this course is for:

  • The person who is a machine learning engineer or a data scientist who wants to learn about new machine learning applications
  • If you’re a professional who wants to know how the products are chosen.

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