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  • رقم الدرس : 31
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  • Machine Learning Regularization with Bias and Variance

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دروس الكورس

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