Machine Learning

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Section: 1 1. Applications of Machine Learning 2. Why Machine Learning is the Future 3. Installing R and R Studio (Windows) 4. Update: Recommended Anaconda Version 5. Installing Python and Anaconda (Windows) Section: 2 -------------------- Part 1: Data Preprocessing -------------------- 6. Welcome to Part 1 - Data Preprocessing 7. Get the dataset 8. Importing the Libraries 9. Importing the Dataset 10. Summary of Object-oriented programming: classes & objects 11. Missing Data 12. Categorical Data 13. Splitting the Dataset into the Training set and Test set 14. Feature Scaling 15. Data Preprocessing Template! Section: 3 -------------------- Part 2: Regression -------------------- 16. Welcome to Part 2 - Regression Section: 4 Simple Linear Regression 17. How to get the dataset 18. Dataset + Business Problem Description 19. Simple Linear Regression Intuition 20. Simple Linear Regression in Python 21. Simple Linear Regression in R Section: 5 Multiple Linear Regression 22. How to get the dataset 23. Dataset + Business Problem Description 24. Multiple Linear Regression Intuition 25. Multiple Linear Regression in Python 26. Multiple Linear Regression in Python - Backward Elimination - Preparation 27. Multiple Linear Regression in Python - Backward Elimination 28. Multiple Linear Regression in R 29. Multiple Linear Regression in R - Backward Elimination Section: 6 Polynomial Regression 30. Polynomial Regression Intuition 31. How to get the dataset 32. Polynomial Regression in Python 33. Python Regression Template 34. Polynomial Regression in R 35. R Regression Template Section: 7 Support Vector Regression (SVR) 36. How to get the dataset 37. SVR in Python 38. SVR in R Section: 8 Decision Tree Regression 39. Decision Tree Regression Intuition 40. How to get the dataset 41. Decision Tree Regression in Python 42. Decision Tree Regression in R Section: 9 Random Forest Regression 43. Random Forest Regression Intuition 44. How to get the dataset 45. Random Forest Regression in Python 46. Random Forest Regression in R Section: 10 Evaluating Regression Models Performance 47. R-Squared Intuition 48. Adjusted R-Squared Intuition 49. Evaluating Regression Models Performance 50. Interpreting Linear Regression Coefficients 51. Conclusion of Part 2 - Regression Section: 11 -------------------- Part 3: Classification -------------------- 52. Welcome to Part 3 - Classification Section: 12 Logistic Regression 53. Logistic Regression Intuition 54. How to get the dataset 55. Logistic Regression in Python 56. Python Classification Template 57. Logistic Regression in R 58. R Classification Template Section: 13 K-Nearest Neighbors (K-NN) 59. K-Nearest Neighbor Intuition 60. How to get the dataset 61. K-NN in Python 62. K-NN in R Section: 14 Support Vector Machine (SVM) 63. SVM Intuition 64. How to get the dataset 65. SVM in Python 66. SVM in R Section: 15 Kernel SVM 67. Kernel SVM Intuition 68. Mapping to a higher dimension 69. The Kernel Trick 70. Types of Kernel Functions 71. How to get the dataset 72. Kernel SVM in Python 73. Kernel SVM in R Section: 16 Naive Bayes 74. Bayes Theorem 75. Naive Bayes Intuition 76. Naive Bayes Intuition (Challenge Reveal) 77. Naive Bayes Intuition (Extras) 78. How to get the dataset 79. Naive Bayes in Python 80. Naive Bayes in R Section: 17 Decision Tree Classification 81. Decision Tree Classification Intuition 82. How to get the dataset 83. Decision Tree Classification in Python 84. Decision Tree Classification in R Section: 18 Random Forest Classification 85. Random Forest Classification Intuition 86. How to get the dataset 87. Random Forest Classification in Python 88. Random Forest Classification in R Section: 19 Evaluating Classification Models Performance 89. False Positives & False Negatives 90. Confusion Matrix 91. Accuracy Paradox 92. CAP Curve 93. CAP Curve Analysis 94. Conclusion of Part 3 - Classification	30 Hours	₹ 6000 | $100	Join now