The Ultimate Beginners Guide to Python Recommender Systems

The Ultimate Beginners Guide to Python Recommender Systems, available at $59.99, has an average rating of 4.64, with 30 lectures, based on 165 reviews, and has 13828 subscribers.

You will learn about 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 This course is ideal for individuals who are People interested in recommender systems or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in recommender systems or Professionals interested in developing recommender systems or Beginners who are starting to learn recommender systems It is particularly useful for People interested in recommender systems or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in recommender systems or Professionals interested in developing recommender systems or Beginners who are starting to learn recommender systems.

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Summary

Title: The Ultimate Beginners Guide to Python Recommender Systems

Price: $59.99

Average Rating: 4.64

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

Target Audiences

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

Recommender systems are a hot topic in ??Artificial Intelligence and are widely used for a lot of companies. They are everywhere recommending movies, music, videos, products, services, and so on. For example, when you finish watching a movie on Netflix, other movies you might like are indicated for you. This is the classic example of a recommender system!

In this course, you will learn in theory and practice how recommender systems work! You will implement an algorithm based on the collaborative filtering technique applied to movie recommendations (user-based filtering and item-based filtering). We are going to use a small dataset to test all mathematical calculations. Then, we will test our algorithm using the famous MovieLens dataset, which has more than 100.000 instances. At the end of the course (after implementing the algorithm from scratch), you will learn how to use two pre-built libraries: LibRecommender and Surprise!

What makes this course unique is that you will implement step by step from scratch in Python, learning all mathematical calculations. This can be considered the first course on recommender systems, so, if you have never heard about how to implement them, at the end you will have all the theoretical and practical background to develop some simple projects and also take more advanced courses. See you in class!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Introduction to recommender systems

Lecture 3: Source code and slides

Chapter 2: Search for similar users

Lecture 1: Movie dataset

Lecture 2: Analyzing users and feedbacks

Lecture 3: Euclidian distance – intuition

Lecture 4: Euclidian distance – implementation 1

Lecture 5: Euclidian distance – implementation 2

Lecture 6: Similarity between users

Chapter 3: Collaborative filtering – user-based filtering

Lecture 1: Recommendations – intuition

Lecture 2: Recommendations – implementation 1

Lecture 3: Recommendations – implementation 2

Lecture 4: Recommendations – implementation 3

Lecture 5: Similar movies – intuition

Lecture 6: Similar movies – implementation

Lecture 7: MovieLens dataset

Lecture 8: Loading the MovieLens dataset

Lecture 9: Recommendations with MovieLens

Lecture 10: Similar movies and MovieLens

Chapter 4: Collaborative filtering – item-based filtering

Lecture 1: Item-based filtering – intuition

Lecture 2: Similarity between movies

Lecture 3: Recommendations – implementation

Lecture 4: MovieLens dataset

Lecture 5: User-based vs Item-base filtering

Chapter 5: Libraries for recommender systems

Lecture 1: Preparing the dataset for LibRecommender

Lecture 2: LibRecommender – user-based filtering

Lecture 3: LibRecommender – item-based filtering

Lecture 4: Surprise library

Chapter 6: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 2 votes

2 stars: 6 votes

3 stars: 25 votes

4 stars: 48 votes

5 stars: 84 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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