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Elgouhary AI

هنا نهتم بتعلم مجالات الذكاء الاصطناعي وتعلم الآلة والتعلم العميق بكل فروعه النظرية والعملية من خلال البرمجة بلغة البايثون وسيتم تغطية كل ما يخص مجال الـ Data Science & Data Analysis وهيكون من خلال الادوات المختلفة لتحقيق ذلك من Excel, Tableau, Power BI, SPSS, SAS وغيرها من الادوات. أرجو منكم صالح الدعاء .

تفاصيل الكورس

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دروس الكورس

  1. 1 | #1 What is Machine Learning 00:06:01
  2. 2 | Machine Learning Supervised Learning 00:16:06
  3. 3 | Machine Learning Unsupervised Learning 00:07:18
  4. 4 | Simple Linear Regression Model 00:07:44
  5. 5 | Machine Learning Linear Regression Cost Function 00:08:22
  6. 6 | Machine Learning Linear Regression Cost Function for One Parameter 00:19:35
  7. 7 | Machine Learning Linear Regression Cost Function for Two Parameters 00:14:08
  8. 8 | Machine Learning Linear Regression Gradient Descent Overview 00:08:08
  9. 9 | Machine Learning Linear Regression Gradient Descent Mathematically 00:12:05
  10. 10 | Machine Learning Gradient Descent for Linear Regression 00:15:46
  11. 11 | Machine Learning Multiple Linear Regression Model 00:09:45
  12. 12 | Machine Learning Multiple Linear Regression Model Feature Scaling 00:13:18
  13. 13 | Machine Learning Checking Gradient Descent for Conversions Choosing the learning rate 00:09:02
  14. 14 | Machine Learning Feature Engineering Polynomial Regression 00:07:26
  15. 15 | Machine Learning Linear Regression vs. Classification 00:06:25
  16. 16 | Machine Learning Logistic Regression 00:07:23
  17. 17 | Machine Learning Decision Boundary 00:08:16
  18. 18 | Machine Learning Cost Function for Logistic Regression 00:17:29
  19. 19 | Machine Learning Overfitting and Underfitting 00:08:47
  20. 20 | Machine Learning How to prevent Overfiting 00:09:29
  21. 21 | Machine Learning Cost Function with Regularization 00:11:31
  22. 22 | Machine Learning Gradient Descent with Regularization 00:08:33
  23. 23 | Machine Learning Introduction to Neural Networks 00:10:07
  24. 24 | Machine Learning Neural Networks in details 00:14:42
  25. 25 | Machine Learning Neural Networks Activation Functions 00:14:55
  26. 26 | Multi-class Classification softmax Regression Multi-Lable Classification 00:17:09
  27. 27 | Machine Learning Advanced Optimization Adam algorithm 00:06:10
  28. 28 | Machine Learning Machine Learning Diagnostic Evaluating The Model 00:14:13
  29. 29 | Machine Learning Model Selection 00:12:17
  30. 30 | Machine Learning Bias and Variance 00:10:24
  31. 31 | Machine Learning Regularization with Bias and Variance 00:08:14
  32. 32 | Machine Learning A Baseline Level of Performance Learning Curves 00:16:00
  33. 33 | Machine Learning Improving The Learning Algorithm 00:12:43
  34. 34 | Machine Learning Introduction to Decision Trees 00:08:21
  35. 35 | Machine Learning Decision Tree Learning Process 00:11:23
  36. 36 | Machine Learning Decision Tree Measuring Purity Entropy Information Gain 00:17:53
  37. 37 | Machine Learning Building a Decision Tree Model 00:08:08
  38. 38 | #38 Machine Learning Decision Tree Using one hot encoding of categorical features 00:05:58
  39. 39 | #39 Machine Learning Decision Tree Continuous Valued Features 00:12:28
  40. 40 | #40 Machine Learning Decision Tree Regression Trees 00:09:11
  41. 41 | #41 Machine Learning Decision Tree Sampling with replacement Random Forest Algo. XGBoost 00:15:09
  42. 42 | #42 Machine Learning Decision Trees vs Neural Networks 00:03:01
  43. 43 | #43 Unsupervised Learning Clustering K-means Intuition 00:11:09
  44. 44 | #44 Unsupervised Learning Anomaly Detection Finding Unusual Events 00:10:08
  45. 45 | #45 Unsupervised Learning Principle Component Analysis PCA 00:07:37
  46. 46 | #46 Machine Learning Introduction to The Reinforcement Learning 00:07:52
  47. 47 | الأساسيات النظرية لتعلم الآلة كاملة في فيديو واحد Machine Learning Complete Course 07:42:49
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    29-07-2024
    تعلم الآلة بالعربي Machine Learning in Arabic

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