کتاب الگوریتم‌های رگرسیون: linear و logistic regression با فرمت AZW3 برای کتابخوان کیندل و فرمت EPUB

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره الگوریتم‌های رگرسیون: linear و logistic regression

موضوع کلی: برنامه نویسی

موضوع میانی: الگوریتم‌ها و ساختمان داده‌ها

📋 سرفصل‌های دوره

• 1. What is Machine Learning?
• 2. Supervised vs. Unsupervised Learning
• 3. Regression vs. Classification Tasks
• 4. Introduction to Regression Algorithms
• 5. The Machine Learning Workflow
• 6. Setting up Your Development Environment (Python, Libraries)
• 7. Introduction to NumPy for Numerical Operations
• 8. Introduction to Pandas for Data Manipulation
• 9. Introduction to Matplotlib for Data Visualization
• 10. Introduction to Seaborn for Enhanced Visualizations
• 11. Variables: Independent, Dependent, Continuous, Discrete
• 12. Measures of Central Tendency: Mean, Median, Mode
• 13. Measures of Dispersion: Variance, Standard Deviation
• 14. Introduction to Probability
• 15. Probability Distributions (Normal Distribution concept)
• 16. Introduction to Linear Algebra: Vectors and Matrices
• 17. Vector Operations: Addition, Scalar Multiplication
• 18. Matrix Operations: Addition, Multiplication
• 19. Dot Product and its Significance
• 20. Introduction to Calculus: Derivatives
• 21. The Concept of a Gradient
• 22. Correlation: Understanding Relationships Between Variables
• 23. Covariance
• 24. Pearson Correlation Coefficient
• 25. Spurious Correlations
• 26. Understanding Your Dataset: Initial Exploration
• 27. Handling Missing Data: Imputation Techniques
• 28. Identifying and Handling Outliers
• 29. Categorical Data Encoding: One-Hot Encoding
• 30. Categorical Data Encoding: Label Encoding
• 31. Feature Scaling: Normalization (Min-Max Scaling)
• 32. Feature Scaling: Standardization (Z-score Scaling)
• 33. Introduction to Feature Engineering
• 34. Creating New Features from Existing Ones
• 35. Data Splitting: Training, Validation, and Test Sets
• 36. Introduction to Simple Linear Regression
• 37. The Linear Model Equation: y = b0 + b1*x
• 38. Assumptions of Linear Regression
• 39. Understanding the Error Term (Residuals)
• 40. The Goal: Minimizing Errors
• 41. Sum of Squared Residuals (SSR)
• 42. The Ordinary Least Squares (OLS) Method
• 43. Derivation of OLS Coefficients for Simple Linear Regression
• 44. Interpreting the Regression Coefficients (Slope and Intercept)
• 45. Implementing Simple Linear Regression from Scratch
• 46. Using Scikit-learn for Simple Linear Regression
• 47. Visualizing the Regression Line
• 48. Understanding Residual Plots
• 49. Homoscedasticity Check
• 50. Normality of Residuals
• 51. Introduction to Multiple Linear Regression
• 52. The Multiple Linear Regression Equation
• 53. Matrix Form of Linear Regression
• 54. The Normal Equation for Multiple Linear Regression
• 55. Gradient Descent: An Optimization Algorithm
• 56. Understanding Learning Rate in Gradient Descent
• 57. Batch Gradient Descent
• 58. Stochastic Gradient Descent
• 59. Mini-Batch Gradient Descent
• 60. Comparing Normal Equation vs. Gradient Descent
• 61. Introduction to Polynomial Regression
• 62. Implementing Polynomial Regression
• 63. Overfitting and Underfitting in Regression
• 64. The Bias-Variance Tradeoff
• 65. Introduction to Regularization
• 66. Mean Absolute Error (MAE)
• 67. Mean Squared Error (MSE)
• 68. Root Mean Squared Error (RMSE)
• 69. R-squared (Coefficient of Determination)
• 70. Adjusted R-squared
• 71. Interpreting R-squared Values
• 72. Introduction to Multicollinearity
• 73. Detecting Multicollinearity with VIF (Variance Inflation Factor)
• 74. Handling Multicollinearity
• 75. Cross-Validation for Robust Model Evaluation
• 76. Introduction to Classification Problems
• 77. Why Linear Regression is Not Suitable for Classification
• 78. The Concept of a Sigmoid (Logistic) Function
• 79. Introduction to Logistic Regression
• 80. The Logistic Regression Hypothesis Function
• 81. Interpreting Probabilities from Sigmoid Output
• 82. Defining the Decision Boundary
• 83. Understanding Likelihood
• 84. Maximum Likelihood Estimation (MLE) for Logistic Regression
• 85. The Cost Function for Logistic Regression (Log Loss / Cross-Entropy)
• 86. Minimizing Log Loss with Gradient Descent
• 87. Derivation of Logistic Regression Gradient
• 88. Implementing Logistic Regression from Scratch
• 89. Using Scikit-learn for Logistic Regression
• 90. Multi-Class Classification: One-vs-Rest Strategy
• 91. Introduction to Classification Metrics
• 92. Confusion Matrix: TP, TN, FP, FN
• 93. Accuracy Score
• 94. Precision
• 95. Recall (Sensitivity)
• 96. F1-Score
• 97. Specificity and Fallout
• 98. ROC Curve and AUC Score
• 99. Setting the Optimal Classification Threshold
• 100. Handling Class Imbalance