AI Mastery: Recommendation Engines Unleashed

AI Mastery: Recommendation Engines Unleashed, available at $19.99, has an average rating of 4.4, with 40 lectures, based on 5 reviews, and has 5697 subscribers.

You will learn about The fundamentals of Recommendation Engines, including collaborative filtering. Setting up the environment with Anaconda, downloading datasets, and using the Surprise library. Implementing cross-validation models for training and testing predictions. Developing functions for making movie predictions and creating a basic Book Recommender. Exploring advanced topics like content-based recommendation and feature extraction. Building an Advanced Book Recommender with hybrid models and user-specific recommendations. Developing a Movie Recommendation Engine, covering simple and content-based recommenders. Throughout the course, students will gain practical experience through hands-on projects, enhancing their skills in building effective recommendation systems. This course is ideal for individuals who are Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering. or Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems. or Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation. or Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models. or Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders. It is particularly useful for Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering. or Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems. or Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation. or Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models. or Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.

Enroll now: AI Mastery: Recommendation Engines Unleashed

Summary

Title: AI Mastery: Recommendation Engines Unleashed

Price: $19.99

Average Rating: 4.4

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

• The fundamentals of Recommendation Engines, including collaborative filtering.
• Setting up the environment with Anaconda, downloading datasets, and using the Surprise library.
• Implementing cross-validation models for training and testing predictions.
• Developing functions for making movie predictions and creating a basic Book Recommender.
• Exploring advanced topics like content-based recommendation and feature extraction.
• Building an Advanced Book Recommender with hybrid models and user-specific recommendations.
• Developing a Movie Recommendation Engine, covering simple and content-based recommenders.
• Throughout the course, students will gain practical experience through hands-on projects, enhancing their skills in building effective recommendation systems.

Who Should Attend

• Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering.
• Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems.
• Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation.
• Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models.
• Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.

Target Audiences

• Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering.
• Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems.
• Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation.
• Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models.
• Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.