محتوى الكورس
40 درساً · 23:10:20- 1 مقدمة في تعلم الآلة ابدأ من هنا 7:04
- 2 Pearson correlation coefficient and simple linear regression 1:44:51
- 3 Gradient descent | data normalization | backward elimination | multi regression 1:54:47
- 4 Normal equation and polynomial regression with python 1:06:23
- 5 overfitting | underfitting | lasso and ridge regression | regularization 1:02:23
- 6 feature selection by Lasso regression 26:36
- 7 logistic regression | Classification 1:10:10
- 8 logistic regression in python and data analysis example 36:21
- 9 SVM and KNN machine learning 48:18
- 10 KNN in python machine learning 5:03
- 11 SVM kernel trick and Decision tree machine learning 30:41
- 12 Decision tree part two and Gini index 35:35
- 13 Build Decision Tree using Gini Index 39:22
- 14 Decision Tree using C4.5 | Gain ration 37:56
- 15 Decision tree in python machine learning 13:05
- 16 Bagging BootStrap aggreggation 10:13
- 17 Random Forest with example in python 15:10
- 18 AdaBoost part one 34:09
- 19 AdaBoost part two 32:40
- 20 AdaBoost part three 26:52
- 21 Gradient Boosting part one 24:04
- 22 Gradient Boosting part two 18:43
- 23 Gradient Boosting part three 10:01
- 24 Gradient Boosting part four 22:10
- 25 XGBoost part one 31:07
- 26 XGBoost part two 39:40
- 27 XGBoost part three 21:43
- 28 Cross Validation and Grid Search 36:00
- 29 Naive Bayes 1:02:39
- 30 Confusion Matrix 33:05
- 31 Eucledian and Manhattan Distance 5:04
- 32 K Means 17:43
- 33 k mean example in python 11:19
- 34 Hierarchical Clusters 5:31
- 35 KNN Algorithm Explained with Simple Example 6:22
- 36 GMM and EM | soft cluster | computer vision 38:52
- 37 DBSCAN Clustering | Computer vision 29:17
- 38 concrete slump using machine learning part one | data analysis | EDA 1:16:39
- 39 concrete slump using machine learning part two | data analysis | EDA 1:02:33
- 40 Precision , Recall , F1-score and Confusion Matrix 20:09
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