Build AI Recommender Systems
Learn Python, AI & ML to create advanced recommender systems. Master content filtering, collaborative filtering & deep learning.
Learn Python, AI & ML to create advanced recommender systems. Master content filtering, collaborative filtering & deep learning.
This comprehensive course teaches you to build recommender systems using Python, AI, machine learning, and deep learning. Starting with fundamentals, you'll progress through content-based filtering, collaborative filtering, and advanced techniques like matrix factorization. The curriculum covers deep learning applications, scalability with Apache Spark, and real-world implementation challenges. You'll learn to evaluate recommendation algorithms, create session-based recommendations using neural networks, and understand systems like YouTube and Netflix. Perfect for developers with basic Python knowledge, this course combines theoretical understanding with practical implementation.
3.9
5,350 already enrolled
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What you'll learn
Analyze and evaluate recommendation algorithms using Python
Implement content-based and collaborative filtering systems
Master matrix factorization and deep learning for recommendations
Create session-based recommendations using neural networks
Scale recommendation computations with Apache Spark
Understand and address real-world recommender system challenges
Skills you'll gain
This course includes:
517 Minutes PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 14 modules in this course
This course provides a comprehensive exploration of recommender systems using Python, AI, and machine learning. Students learn to build recommendation engines from simple to complex hybrid systems. The curriculum covers essential concepts including content-based filtering, collaborative filtering, matrix factorization, and deep learning applications. Practical implementation focuses on using Python and frameworks like Apache Spark for scalability. The course addresses real-world challenges, studies successful systems like YouTube and Netflix, and emphasizes hands-on experience through assignments and activities.
Getting Started
Module 1 · 44 Minutes to complete
Introduction to Python
Module 2 · 16 Minutes to complete
Evaluating a Recommender System
Module 3 · 54 Minutes to complete
A Recommender Engine Framework
Module 4 · 18 Minutes to complete
Content-Based Filtering
Module 5 · 31 Minutes to complete
Neighborhood-Based Collaborative Filtering
Module 6 · 1 Hours to complete
Matrix Factorization Methods
Module 7 · 27 Minutes to complete
Introduction to Deep Learning
Module 8 · 3 Hours to complete
Deep Learning for Recommender Systems
Module 9 · 2 Hours to complete
Scaling It Up
Module 10 · 1 Hours to complete
Real-World Challenges of Recommender Systems
Module 11 · 50 Minutes to complete
Case Studies
Module 12 · 18 Minutes to complete
Hybrid Approaches
Module 13 · 22 Minutes to complete
Wrapping Up
Module 14 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
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3.9 course rating
Frequently asked questions
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