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  • Lesson.No : 19
  • 00:34:01

  • Lecture 12 : Data splitting and Cross validation techniques

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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)