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Mammoth Club All levels 14 sections 19 lectures

Video Recommender Systems with Python & NumPy: Practical Data Mining Bootcamp with 10 Exams

Recommendation engines power much of the content we see online.

01
Skill level
All levels
02
Sections
14
03
Lectures
19
04
Instructor
John Bura
What's inside

This course includes.

14
Sections
19
Lectures
7
Resources
11
Quizzes
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

4 sections · 9 lectures
+ Test Your Knowledge! 1 lecture
Practice Code Exam Locked
+ Challenge Your 10 FREE Practice Exams 1 lecture
Where to Find Your Exams Locked
+ Default Lesson 2 lectures
00 Course Overview Locked
01 Project Preview Locked
+ Build a Video Recommender with Data Mining 5 lectures
01 Build A Dataset Locked
02 Compute Support And Confidence - If A Person Watches X, They Will Watch Y Locked
03 Compute Support And Confidence For All Channels Locked
04 Determine Which Videos Are Best To Recommend Locked
Source Files Locked
Description

About this course.

This course introduces the fundamentals of building a simple video recommender system using Python and NumPy, guiding you through practical workflows in data mining.


✅ Explore dataset preparation and similarity measures

✅ Use NumPy for handling arrays and computations

✅ Build and test basic recommender models

✅ Reinforce concepts with 10 structured exams


By applying methods in a focused use case, you’ll learn transferable skills for personalization systems across domains.


🎁 Human-created tutorials improved with AI, reviewed by human editors and enriched with real-time chat guidance and quizzes that keep you motivated and engaged. Discover how data powers the systems that guide digital experiences.

Instructors

Taught by people who ship.

John Bura

John Bura

Founder and CEO of Mammoth Club and Course Pro, the #1 AI-powered Learning Management System for course and content development, training and evaluation.

Ready to start building?

Recommendation engines power much of the content we see online.

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