March 2023
Intermediate to advanced
6h 16m
English
Closed Captioning available in English
Overview
In this 6 hr course, you'll learn the theoretical foundations and practical applications of building recommender systems using Python. You'll explore content-based and collaborative filtering techniques with real-world projects, such as a movie recommender or music recommendation system.
What I will be able to do after this course
- Understand the fundamentals of recommender systems essential taxonomies.
- Design and implement content-based and collaborative filtering recommender systems.
- Use Python to analyze data related to recommendations (e.g., user preferences and behaviors).
- Explore overfitting, underfitting, and ways to improve the accuracy of recommender models.
- Apply machine learning concepts to practical projects like movie and music recommender systems.
Course Instructor(s)
The instructors at AI Sciences possess expertise in the field of artificial intelligence, data analysis, and educational content creation. They prioritize simplifying complex topics, providing engaging learning experiences, and equipping learners with the tools necessary to excel in a competitive industry.
Who is it for?
This course is ideal for aspiring data scientists, machine learning practitioners, and developers with basic Python knowledge. It is specifically designed for beginners aiming to learn about recommender systems and those seeking practical experience with machine learning techniques. If you want to understand and build real-world recommender systems, this course is for you.
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