From Theory to Implementation
Description
Principal Component Analysis (PCA) is an unsupervised learning algorithms and it is mainly used for dimensionality reduction, lossy data compression and feature extraction. It is the mostly used unsupervised learning algorithm in the field of Machine Learning.
Join our Telegram for instant 100% OFF alerts 👉 t.me/coupontex
In this video tutorial, after reviewing the theoretical foundations of Principal Component Analysis (PCA), this method is implemented step-by-step in Python and MATLAB. Also, PCA is performed on Iris Dataset and images of hand-written numerical digits, using Scikit-Learn (Python library for Machine Learning) and Statistics Toolbox of MATLAB. Also the projects files are available to download at the end of this post.
Join our Telegram for instant 100% OFF alerts 👉 t.me/coupontex
| Total Students | 13665 |
|---|---|
| Duration | 1.5 hours |
| Language | English (US) |
| Number of lectures | 9 |
| Number of quizzes | 0 |
| Total Reviews | 163 |
| Global Rating | 4.43 |
| Instructor Name | Yarpiz Team |
Course Insights (for Students)
Actionable, non-generic pointers before you enroll
Student Satisfaction
86% positive recent sentiment
Momentum
🔥 Trending
Time & Value
- Est. time: 1.5 hours
- Practical value: 7/10
Roadmap Fit
- Beginner → Beginner → Advanced
Key Takeaways for Learners
- Hands-on practice
- Real-world examples
- Project-based learning
- Hands On
- Clear Explanation
Course Review Summary
Signals distilled from the latest Udemy reviews
What learners praise
- Hands On
- Clear Explanation
- Step By Step
- Examples
- Real World
Watch-outs
- Too fast
- Too slow
- Theory only
Difficulty
Best suited for
New learners starting from zero, Learners who like theory + frameworks
Reminder – Rate this 100% off Udemy Course on Udemy that you got for FREEE!!
Join our Telegram for instant 100% OFF alerts 👉 t.me/coupontex
