Dimensionality Reduction: Principal Component Analysis (PCA)

Course Lessons

1. 1- Machine Learning-1: Probability and Statistics Basics
2. 2- Machine Learning-2: Statistics and Probability (part-2)
3. 3- Machine Learning-3: Linear Algebra (part-1)
4. 4- Machine-Learning-4: Linear Algebra (part-2)
5. 5- Machine Learning-5: Linear Algebra (part-3)
6. 6- Machine Learning-6: Linear Algebra (part-4)
7. 7- Machine Learning-7: Linear Algebra (part-5)
8. 8- مسارات تعليم الالة ومقارنة بينها وبين علم البيانات والذكاء الاصطناعي
9. 9- Dataset Search Using Google Dataset
10. 10- Machine Learning Brief History and Some Examples
11. 11- ML terminology, Algorithms, and the Bayesian Decision Theory
12. 12- Discriminant Function and Normal Density
13. 13- Parametric Estimation in Machine Learning
14. 14- ما هو تعليم الالة؟ What is Machine Learning
15. 15- Unsupervised Learning and Clustering
16. 16- K-means Algorithm with examples
17. 17- Supervised Learning and KNN Algorithm
18. 18- Non-parametric Learning: k-Nearest Neighbor (kNN)
19. 19- Dimensionality Reduction using Fisher Linear Discriminant (FLD)
20. 20- Dimensionality Reduction: Principal Component Analysis (PCA)
21. 21- Performance Evaluation in Machine Learning
22. 22- Gradient Descent in Machine Learning with MATLAB Example
23. 23- Support Vector Machine (SVM) and VC dimension
24. 24- Non-Separable SVM and libsvm example with MATLAB
25. 25- Machine Learning Course: Classifier Fusion
26. 26- Machine Learning Course: Unsupervised Learning (Clustering)
27. 27- Machine Learning Course: Decision Trees and Random Forests
28. 28- AI Roadmap دليلك للبدء في عالم الذكاء الاصطناعي
29. 29- الفرق بين علم البيانات والتعلم الالي والتعلم العميق والذكاء الاصطناعي بأختصار
30. 30- Artificial Intelligence Roadmap 2022 (in English)
31. 31- ما هو الحجم المثالي لمجموعة البيانات لتدريب نماذج الذكاء الاصطناعي؟
32. 32- Knowledge Distillation in Machine Learning تقطير المعرفة في عالم الذكاء الاصطناعي