The Ultimate Beginners Guide to Python Recommender Systems paid course free. You will Use collaborative filtering to recommend movies to users! Implementations step by step from scratch!
- Understand the basics about recommender systems
- Understand the theory and mathematical calculations of collaborative filtering
- Implement user-based collaborative filtering and item-based collaborative filtering step by step in Python
- Use the following libraries for recommender systems: LibRecommender and Surprise
- Use the MovieLens dataset to generate movie recommendations for users
The Ultimate Beginners Guide to Python Recommender Systems Course Requirements
- Programming logic
- Basic Python programming
The Ultimate Beginners Guide to Python Recommender Systems Course Description
Recommender frameworks are a hotly debated issue in Artificial Intelligence and are broadly utilized for a great deal of organizations. They are wherever suggesting films, music, recordings, items, administrations, etc. For instance, when you wrap up watching a film on Netflix, different motion pictures you may like are demonstrated for you. This is the exemplary illustration of a recommender framework!
In this course, you will learn in principle and practice how recommender frameworks work! You will execute a calculation dependent on the synergistic sifting method applied to film proposals (client based separating and thing based sifting). We will utilize a little dataset to test every single numerical computation. Then, at that point, we will test our calculation utilizing the popular MovieLens dataset, which has more than 100.000 occasions. Toward the finish of the course (subsequent to carrying out the calculation without any preparation), you will figure out how to utilize two pre-fabricated libraries: LibRecommender and Surprise!
What makes this course remarkable is that you will carry out bit by bit without any preparation in Python, learning every single numerical computation.
This can be viewed as the primary seminar on recommender frameworks, along these lines, in the event that you have never found out about how to execute them, toward the end you will have all the hypothetical and commonsense foundation to foster some straightforward tasks and furthermore take further developed courses. See you in class!
Who this course is for:
- People interested in recommender systems
- Students who are studying subjects related to Artificial Intelligence
- Data Scientists who want to increase their knowledge in recommender systems
- Professionals interested in developing recommender systems
- Beginners who are starting to learn recommender systems