Packt
Recommender Systems with Machine Learning
Packt

Recommender Systems with Machine Learning

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the basics of AI-integrated recommender systems

  • Analyze the impact of overfitting, underfitting, bias, and variance

  • Apply machine learning and Python to build content-based recommender systems

  • Create and model a KNN-based recommender engine for applications

Details to know

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Recently updated!

September 2024

Assessments

3 assignments

Taught in English

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There are 6 modules in this course

In this module, we will introduce you to the field of AI Sciences and recommender systems. You will meet the instructor, explore the course layout, understand the basics of recommender systems, and preview the exciting projects you will undertake.

What's included

5 videos1 reading

In this module, we will delve into the motivations behind recommender systems. You will learn about their processes, historical evolution, and the critical role AI plays. We'll also cover practical applications and the challenges faced in real-world scenarios.

What's included

8 videos

In this module, we will cover the foundational aspects of recommender systems. You will study the taxonomy, data matrices, evaluation techniques, and filtering methods, equipping you with a solid understanding of how these systems function and are assessed.

What's included

15 videos1 assignment

In this module, we will focus on leveraging machine learning for recommender systems. You will gain insights into data preparation, explore filtering methods, and implement machine learning algorithms like tf-idf and KNN, enhancing the recommendation process.

What's included

21 videos

In this module, we will guide you through building a song recommendation system using content-based filtering. You will work on dataset management, genre exploration, and implement advanced techniques like tf-idf and FuzzyWuzzy to create effective song recommendations.

What's included

10 videos

In this module, we will take you through developing a movie recommendation system using collaborative filtering. You will learn to analyze user and movie data, create collaborative filters, and apply KNN to generate accurate movie recommendations, culminating the course with practical applications.

What's included

10 videos2 assignments

Instructor

Packt
Packt
170 Courses2,469 learners

Offered by

Packt

Recommended if you're interested in Data Analysis

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