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[] Udemy - Machine Learning Basics Building a Regression model in R

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  • 文件大小:3GB
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文件列表

  1. 6. Linear Regression Model/20. Ridge regression and Lasso in R.mp4 124MB
  2. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 124MB
  3. 3. Getting started with R and R studio/7. Creating Barplots in R.mp4 118MB
  4. 5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4 114MB
  5. 5. Data Preprocessing/7. EDD in R.mp4 112MB
  6. 6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.mp4 104MB
  7. 3. Getting started with R and R studio/3. Packages in R.mp4 99MB
  8. 5. Data Preprocessing/24. Correlation Matrix in R.mp4 95MB
  9. 6. Linear Regression Model/14. Test-Train Split in R.mp4 91MB
  10. 6. Linear Regression Model/16. Subset Selection techniques.mp4 87MB
  11. 2. Basics of Statistics/3. Describing the data graphically.mp4 82MB
  12. 5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.mp4 81MB
  13. 5. Data Preprocessing/3. The Data and the Data Dictionary.mp4 79MB
  14. 6. Linear Regression Model/17. Subset selection in R.mp4 77MB
  15. 6. Linear Regression Model/10. Multiple Linear Regression in R.mp4 73MB
  16. 3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.mp4 69MB
  17. 5. Data Preprocessing/17. Variable transformation in R.mp4 68MB
  18. 6. Linear Regression Model/8. The F - statistic.mp4 64MB
  19. 5. Data Preprocessing/21. Dummy variable creation in R.mp4 52MB
  20. 3. Getting started with R and R studio/8. Creating Histograms in R.mp4 52MB
  21. 6. Linear Regression Model/5. Simple Linear Regression in R.mp4 51MB
  22. 6. Linear Regression Model/2. Basic equations and Ordinary Least Squared (OLS) method.mp4 50MB
  23. 6. Linear Regression Model/4. Assessing Model Accuracy - RSE and R squared.mp4 50MB
  24. 6. Linear Regression Model/12. Test-Train split.mp4 49MB
  25. 3. Getting started with R and R studio/2. Basics of R and R studio.mp4 48MB
  26. 1. Introduction/2. Course contents.mp4 47MB
  27. 3. Getting started with R and R studio/4. Inputting data part 1 Inbuilt datasets of R.mp4 46MB
  28. 2. Basics of Statistics/4. Measures of Centers.mp4 46MB
  29. 4. Introduction to Machine Learning/2. Building a Machine Learning model.mp4 45MB
  30. 3. Getting started with R and R studio/1. Installing R and R studio.mp4 41MB
  31. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 41MB
  32. 6. Linear Regression Model/7. Multiple Linear Regression.mp4 39MB
  33. 6. Linear Regression Model/19. Shrinkage methods - Ridge Regression and The Lasso.mp4 39MB
  34. 5. Data Preprocessing/10. Outlier Treatment in R.mp4 38MB
  35. 5. Data Preprocessing/13. Missing Value imputation in R.mp4 32MB
  36. 3. Getting started with R and R studio/5. Inputting data part 2 Manual data entry.mp4 31MB
  37. 6. Linear Regression Model/13. Bias Variance trade-off.mp4 30MB
  38. 2. Basics of Statistics/6. Measures of Dispersion.mp4 28MB
  39. 5. Data Preprocessing/9. Outlier Treatment.mp4 28MB
  40. 5. Data Preprocessing/12. Missing Value imputation.mp4 28MB
  41. 5. Data Preprocessing/6. Univariate Analysis and EDD.mp4 27MB
  42. 6. Linear Regression Model/9. Interpreting result for categorical Variable.mp4 27MB
  43. 2. Basics of Statistics/1. Types of Data.mp4 26MB
  44. 5. Data Preprocessing/1. Gathering Business Knowledge.mp4 25MB
  45. 5. Data Preprocessing/19. Non Usable Variables.mp4 24MB
  46. 5. Data Preprocessing/2. Data Exploration.mp4 23MB
  47. 5. Data Preprocessing/15. Seasonality in Data.mp4 21MB
  48. 6. Linear Regression Model/15. Linear models other than OLS.mp4 19MB
  49. 5. Data Preprocessing/4. Importing the dataset into R.mp4 16MB
  50. 1. Introduction/1. Welcome to the course!.mp4 15MB
  51. 2. Basics of Statistics/2. Types of Statistics.mp4 13MB
  52. 6. Linear Regression Model/1. The problem statement.mp4 11MB
  53. 5. Data Preprocessing/19. Non Usable Variables.vtt 2MB
  54. 4. Introduction to Machine Learning/1.1 Lecture_machineLearning.pdf.pdf 992KB
  55. 4. Introduction to Machine Learning/2.1 Lecture_machineLearning.pdf.pdf 992KB
  56. 1. Introduction/2.1 00_Introduction_01.pdf.pdf 791KB
  57. 2. Basics of Statistics/5.1 Exercise 1.pdf.pdf 554KB
  58. 2. Basics of Statistics/7.1 Exercise 2.pdf.pdf 470KB
  59. 5. Data Preprocessing/16.1 04_07_Variable_Transformation.pdf.pdf 423KB
  60. 5. Data Preprocessing/15.1 04_07_PDE_Seasonality.pdf.pdf 364KB
  61. 5. Data Preprocessing/9.1 04_06_PDE_Outlier_Treatment.pdf.pdf 355KB
  62. 5. Data Preprocessing/6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf 333KB
  63. 6. Linear Regression Model/3.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf 333KB
  64. 6. Linear Regression Model/4.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf 333KB
  65. 5. Data Preprocessing/3.2 03_03_PDE_Raw_Data_Analysis_Uni.pdf.pdf 332KB
  66. 6. Linear Regression Model/8.1 05_05_F_stat.pdf.pdf 328KB
  67. 5. Data Preprocessing/2.1 03_02_PDE_Data_exploration.pdf.pdf 323KB
  68. 2. Basics of Statistics/3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf 318KB
  69. 5. Data Preprocessing/12.1 04_05_PDE_Missing_value.pdf.pdf 316KB
  70. 2. Basics of Statistics/4.1 01_04_Lecture_Centers.pdf.pdf 313KB
  71. 6. Linear Regression Model/2.1 05_02_Simple_lin_reg.pdf.pdf 285KB
  72. 5. Data Preprocessing/23.1 04_10_Correlation.pdf.pdf 257KB
  73. 6. Linear Regression Model/1.1 05_01_Intro.pdf.pdf 239KB
  74. 6. Linear Regression Model/12.1 05_12_Test_Train.pdf.pdf 239KB
  75. 6. Linear Regression Model/7.1 05_04_Multiple_lin_reg.pdf.pdf 220KB
  76. 2. Basics of Statistics/6.1 01_05_Lecture_Dispersion.pdf.pdf 211KB
  77. 6. Linear Regression Model/13.1 05_13_Bias_Var_tradeoff.pdf.pdf 202KB
  78. 6. Linear Regression Model/16.1 05_10_Subset_Selection.pdf.pdf 199KB
  79. 6. Linear Regression Model/19.1 05_11_Shrinkage_methods.pdf.pdf 188KB
  80. 2. Basics of Statistics/1.1 01_01_Lecture_TypesOfData.pdf.pdf 178KB
  81. 2. Basics of Statistics/2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf 172KB
  82. 5. Data Preprocessing/20.1 04_11_Dummy_Var.pdf.pdf 163KB
  83. 6. Linear Regression Model/15.1 05_09_Other_lin_model.pdf.pdf 157KB
  84. 6. Linear Regression Model/9.1 05_06_Cat_var.pdf.pdf 155KB
  85. 5. Data Preprocessing/1.1 03_01_PDE_Business_knowledge.pdf.pdf 154KB
  86. 5. Data Preprocessing/19.1 04_08_PDE_Non_Usable_var.pdf.pdf 138KB
  87. 5. Data Preprocessing/3.1 House_Price.csv.csv 53KB
  88. 5. Data Preprocessing/4.1 House_Price.csv.csv 53KB
  89. 5. Data Preprocessing/5.1 Movie_collection_train.csv.csv 43KB
  90. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.vtt 21KB
  91. 3. Getting started with R and R studio/7. Creating Barplots in R.vtt 16KB
  92. 5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.vtt 16KB
  93. 6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.vtt 14KB
  94. 3. Getting started with R and R studio/3. Packages in R.vtt 13KB
  95. 3. Getting started with R and R studio/2. Basics of R and R studio.vtt 13KB
  96. 6. Linear Regression Model/22.1 Movie_collection_test.csv.csv 12KB
  97. 4. Introduction to Machine Learning/2. Building a Machine Learning model.vtt 12KB
  98. 2. Basics of Statistics/3. Describing the data graphically.vtt 11KB
  99. 6. Linear Regression Model/16. Subset Selection techniques.vtt 11KB
  100. 5. Data Preprocessing/7. EDD in R.vtt 10KB
  101. 6. Linear Regression Model/20. Ridge regression and Lasso in R.vtt 10KB
  102. 5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.vtt 10KB
  103. 1. Introduction/2. Course contents.vtt 9KB
  104. 6. Linear Regression Model/12. Test-Train split.vtt 9KB
  105. 6. Linear Regression Model/2. Basic equations and Ordinary Least Squared (OLS) method.vtt 9KB
  106. 5. Data Preprocessing/24. Correlation Matrix in R.vtt 8KB
  107. 6. Linear Regression Model/8. The F - statistic.vtt 8KB
  108. 5. Data Preprocessing/17. Variable transformation in R.vtt 8KB
  109. 3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.vtt 7KB
  110. 6. Linear Regression Model/14. Test-Train Split in R.vtt 7KB
  111. 6. Linear Regression Model/19. Shrinkage methods - Ridge Regression and The Lasso.vtt 7KB
  112. 6. Linear Regression Model/10. Multiple Linear Regression in R.vtt 7KB
  113. 6. Linear Regression Model/5. Simple Linear Regression in R.vtt 7KB
  114. 6. Linear Regression Model/4. Assessing Model Accuracy - RSE and R squared.vtt 7KB
  115. 5. Data Preprocessing/3. The Data and the Data Dictionary.vtt 7KB
  116. 3. Getting started with R and R studio/8. Creating Histograms in R.vtt 7KB
  117. 6. Linear Regression Model/17. Subset selection in R.vtt 7KB
  118. 3. Getting started with R and R studio/1. Installing R and R studio.vtt 7KB
  119. 2. Basics of Statistics/4. Measures of Centers.vtt 6KB
  120. 6. Linear Regression Model/13. Bias Variance trade-off.vtt 6KB
  121. 6. Linear Regression Model/7. Multiple Linear Regression.vtt 5KB
  122. 3. Getting started with R and R studio/4. Inputting data part 1 Inbuilt datasets of R.vtt 5KB
  123. 2. Basics of Statistics/6. Measures of Dispersion.vtt 5KB
  124. 6. Linear Regression Model/9. Interpreting result for categorical Variable.vtt 5KB
  125. 5. Data Preprocessing/21. Dummy variable creation in R.vtt 5KB
  126. 2. Basics of Statistics/1. Types of Data.vtt 4KB
  127. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.vtt 4KB
  128. 5. Data Preprocessing/9. Outlier Treatment.vtt 4KB
  129. 6. Linear Regression Model/15. Linear models other than OLS.vtt 4KB
  130. 5. Data Preprocessing/10. Outlier Treatment in R.vtt 4KB
  131. 5. Data Preprocessing/12. Missing Value imputation.vtt 4KB
  132. 5. Data Preprocessing/1. Gathering Business Knowledge.vtt 3KB
  133. 5. Data Preprocessing/15. Seasonality in Data.vtt 3KB
  134. 3. Getting started with R and R studio/5. Inputting data part 2 Manual data entry.vtt 3KB
  135. 5. Data Preprocessing/2. Data Exploration.vtt 3KB
  136. 1. Introduction/1. Welcome to the course!.vtt 3KB
  137. 5. Data Preprocessing/6. Univariate Analysis and EDD.vtt 3KB
  138. 5. Data Preprocessing/13. Missing Value imputation in R.vtt 3KB
  139. 2. Basics of Statistics/2. Types of Statistics.vtt 3KB
  140. 5. Data Preprocessing/4. Importing the dataset into R.vtt 2KB
  141. 6. Linear Regression Model/23. Course Conclusion.html 2KB
  142. 6. Linear Regression Model/1. The problem statement.vtt 1KB
  143. 5. Data Preprocessing/5. Project Exercise 1.html 431B
  144. 6. Linear Regression Model/21. Project Exercise 11.html 398B
  145. 2. Basics of Statistics/5. Practice Exercise 1.html 354B
  146. 6. Linear Regression Model/22. Final Project Exercise.html 329B
  147. 6. Linear Regression Model/11. Project Exercise 9.html 327B
  148. 6. Linear Regression Model/6. Project Exercise 8.html 322B
  149. 2. Basics of Statistics/7. Practice Exercise 2.html 295B
  150. 5. Data Preprocessing/25. Project Exercise 7.html 288B
  151. 5. Data Preprocessing/18. Project Exercise 5.html 286B
  152. 5. Data Preprocessing/14. Project Exercise 4.html 238B
  153. 5. Data Preprocessing/11. Project Exercise 3.html 233B
  154. 5. Data Preprocessing/22. Project Exercise 6.html 202B
  155. 6. Linear Regression Model/18. Project Exercise 10.html 199B
  156. 5. Data Preprocessing/8. Project Exercise 2.html 177B
  157. 4. Introduction to Machine Learning/3. Introduction to Machine learning quiz.html 163B
  158. [DesireCourse.Com].url 51B