Complete Machine Learning with R Studio - ML for 2020

Topic: Complete Machine Learning with R Studio - ML for 2020 Course content Section 1: Welcome to the course 1. Introduction 2. Course resources: Notes and Datasets (Part 1) Section 2: Setting up R Studio and R crash course 3. Installing R and R studio 4. Basics of R and R studio 5. Packages in R 6. Inputting data part 1: Inbuilt datasets of R 7. Inputting data part 2: Manual data entry 8. Inputting data part 3: Importing from CSV or Text files 9. Creating Barplots in R 10. Creating Histograms in R Section 3: Basics of Statistics 11. Types of Data 12. Types of Statistics 13. Describing the data graphically 14. Measures of Centers 15. Measures of Dispersion Section 4: Intorduction to Machine Learning 16. Introduction to Machine Learning 17. Building a Machine Learning Model Section 5: Data Preprocessing for Regression Analysis 18. Gathering Business Knowledge 19. Data Exploration 20. The Data and the Data Dictionary 21. Importing the dataset into R 22. Univariate Analysis and EDD 23. EDD in R 24. Outlier Treatment 25. Outlier Treatment in R 26. Missing Value imputation 27. Missing Value imputation in R 28. Seasonality in Data 29. Bi-variate Analysis and Variable Transformation 30. Variable transformation in R 31. Non Usable Variables 32. Dummy variable creation: Handling qualitative data 33. Dummy variable creation in R 34. Correlation Matrix and cause-effect relationship 35. Correlation Matrix in R Section 6: Linear Regression Model 36. The problem statement 37. Basic equations and Ordinary Least Squared (OLS) method 38. Assessing Accuracy of predicted coefficients 39. Assessing Model Accuracy - RSE and R squared 40. Simple Linear Regression in R 41. Multiple Linear Regression 42. The F - statistic 43. Interpreting result for categorical Variable 44. Multiple Linear Regression in R 45. Test-Train split 46. Bias Variance trade-off 47. Test-Train Split in R Section 7: Regression models other than OLS 48. Linear models other than OLS 49. Subset Selection techniques 50. Subset selection in R 51. Shrinkage methods - Ridge Regression and The Lasso 52. Ridge regression and Lasso in R Section 8: Classification Models: Data Preparation 53. The Data and the Data Dictionary 54. Course resources: Notes and Datasets 55. Importing the dataset into R 56. EDD in R 57. Outlier Treatment in R 58. Missing Value imputation in R 59. Variable transformation in R 60. Dummy variable creation in R Section 9: The Three classification models 61. Three Classifiers and the problem statement 62. Why can't we use Linear Regression? Section 10: Logistic Regression 63. Logistic Regression 64. Training a Simple Logistic model in R 65. Results of Simple Logistic Regression 66. Logistic with multiple predictors 67. Training multiple predictor Logistic model in R 68. Confusion Matrix 69. Evaluating Model performance 70. Predicting probabilities, assigning classes and making Confusion Matrix in R Section 11: Linear Discriminant Analysis 71. Linear Discriminant Analysis 72. Linear Discriminant Analysis in R Section 12: K-Nearest Neighbors 73. Test-Train Split 74. Test-Train Split in R 75. K-Nearest Neighbors classifier 76. K-Nearest Neighbors in R Section 13: Comparing results from 3 models 77. Understanding the results of classification models 78. Summary of the three models Section 14: Simple Decision Trees 79. Basics of Decision Trees 80. Understanding a Regression Tree 81. The stopping criteria for controlling tree growth 82. The Data set for this part 83. Course resources: Notes and Datasets 84. Importing the Data set into R 85. Splitting Data into Test and Train Set in R 86. Building a Regression Tree in R 87. Pruning a tree 88. Pruning a Tree in R Section 15: Simple Classification Tree 89. Classification Trees 90. The Data set for Classification problem 91. Building a classification Tree in R 92. Advantages and Disadvantages of Decision Trees Section 16: Ensemble technique 1 - Bagging 93. Bagging 94. Bagging in R Section 17: Ensemble technique 2 - Random Forest 95. Random Forest technique 96. Random Forest in R Section 18: Ensemble technique 3 - GBM, AdaBoost and XGBoost 97. Boosting techniques 98. Gradient Boosting in R 99. AdaBoosting in R 100. XGBoosting in R Section 19: Maximum Margin Classifier 101. Content flow 102. The Concept of a Hyperplane 103. Maximum Margin Classifier 104. Limitations of Maximum Margin Classifier Section 20: Support Vector Classifier 105. Support Vector classifiers 106. Limitations of Support Vector Classifiers Section 21: Support Vector Machines 107. Kernel Based Support Vector Share to help us. Channel Link: https://bit.ly/39YRQBK