What you'll get
- 5+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Understand the fundamentals of Recommendation Engines
- Learn techniques used by companies like Netflix to suggest movies to users
- Build a simple, functional recommendation system from scratch
- Apply recommendation systems to suggest movies, books, and other items
- Explore Collaborative Filtering methods for personalized recommendations
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Recommendation Engine: Recommending Movies | 41m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Recommendation Engine: Book Recommender | 2h 28m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Recommendation Engine: Advanced Book Recommender | 1h 43m | ✔ | View Curriculum |
| Develop Movie Recommendation Engine using Machine Learning | 51m | ✔ | View Curriculum |
Description
This course provides a comprehensive introduction to Recommendation Systems and Recommendation Engines using Python. Companies like Netflix, Amazon, and YouTube use recommendation engines to suggest movies, products, and content tailored to individual user preferences. These systems identify what users like and suggest items they are likely to enjoy, helping boost engagement and sales.
In this course, you will:
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Learn the basics of how recommendation systems work
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Learn how Collaborative Filtering and Content-Based Filtering work
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Explore how companies like Netflix use collaborative filtering to recommend movies based on similar users' tastes
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Build a simple, functional recommendation engine from scratch using Python and basic math
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Get hands-on experience with easy-to-moderate techniques to create practical recommendation systems.
By the end of this course, you will have a conceptual and practical understanding of recommendation engines, equipping you to implement them in real-world applications and preparing you for more advanced studies in the field.
Requirements
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Basic knowledge of Python programming
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Anaconda and Python are installed on your PC
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Basic understanding of data structures like lists, dictionaries, and arrays
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Basic knowledge of statistics is helpful but not mandatory
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Access to a computer or laptop for hands-on practice.
Target Audience
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Professionals who want to understand how product recommendation systems work
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Python beginners looking for practical, hands-on projects
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Data enthusiasts interested in building personalized recommendation engines
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Students and developers are eager to apply machine learning in real-world applications.
