Supervised/Unsupervised Machine Learning Projects
Real-World Projects Based on Clients and Products
Updated on Feb, 2026
Language - English
Lectures -14
Resources -1
Duration -4.5 hours
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Course Description
This course is a compilation of both Supervised and Unsupervised Machine Learning courses: I created it for those who want a unified experience without having to jump between separate courses.
Supervised:
Supervised:
- House Pricing (Regression)
You’ll apply regression models to predict property values based on location, size, age, and other features, using real data and deep exploratory analysis techniques. - Customer Churn (Classification)
You’ll implement classification models to identify patterns that indicate a customer might leave a service, optimizing retention strategies with advanced metrics and predictive modeling.
- Customer Segmentation
You’ll apply clustering models to group customers based on their behavior and features, optimizing business and marketing strategies through advanced multidimensional analysis. - Product Recommendation System
You’ll develop recommendation systems based on segmentation and consumption patterns, enhancing personalization using collaborative algorithms and hands-on implementation.
- It’s for those who haven't taken my previous courses and want access to all the content in a concise, project-driven format.
- It also suits learners who prefer a practical and unified structure.
Goals
- Housing Valuation
- Customer Segmentation
Prerequisites
- People interested in Machine Learning projects
- Python
- Data Science
Curriculum
Check out the detailed breakdown of what’s inside the course
Recommender
8 Lectures
-
1
20:31
20:31
-
2
22:16
22:16
-
3
15:36
15:36
-
4
13:06
13:06
-
5
17:30
17:30
-
6
20:17
20:17
-
7
23:49
23:49
-
8
26:43
26:43
Segmentation
6 Lectures
Instructor Details
Aaron Sánchez
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