Lecture 12 : Data splitting and Cross validation techniques

Course Lessons

1. 1- Lecture 1 : Introduction to machine learning Part 1
2. 2- Lecture 1 : introduction to machine learning Part 2
3. 3- Lecture 2 : Linear Regression with one variable Part 1
4. 4- Lecture 2 : Linear Regression with one variable Part 2
5. 5- Lecture 2 : Linear Regression with one variable Part 3
6. 6- Lecture 3 : Linear Regression with multiple variables Part 1
7. 7- Lecture 3 : Linear Regression with multiple variables Part 2
8. 8- Lecture 4 : Logistic Regression Part 1
9. 9- Lecture 4 : Logistic Regression Part 2
10. 10- Lecture 5: Support Vector Machine Part 1
11. 11- Lecture 5 : Support Vector Machine Part 2
12. 12- Lecture 5 : Support Vector Machine Part 3
13. 13- Lecture 6: Kmeans Clustering Algorithm بالعربى
14. 14- Lecture 7 : Niave Bayes Algorithm بالعربى
15. 15- Lecture 8 : K Nearest Neighbours Algorithm بالعربى
16. 16- Lecture 9 : DecisionTree Algorithm بالعربى
17. 17- Lecture 10 : Random Forest Algorithm بالعربى
18. 18- Lecture 11 : Hierarchal Clustering Algorithm بالعربى
19. 19- Lecture 12 : Data splitting and Cross validation techniques
20. 20- Overfitting, Underfitting And Regularization techniques عربى
21. 21- Linear Regression without Sklearn (Gradient Descent Implementation)