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[] Udemy - Complete Linear Regression Analysis in Python

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  • 文件大小:3GB
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文件列表

  1. 6. Linear Regression/22. Ridge regression and Lasso in Python.mp4 157MB
  2. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 124MB
  3. 5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 114MB
  4. 6. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 104MB
  5. 6. Linear Regression/11. Multiple Linear Regression in Python.mp4 88MB
  6. 6. Linear Regression/20. Subset selection techniques.mp4 87MB
  7. 5. Data Preprocessing/10. Outlier Treatment in Python.mp4 87MB
  8. 5. Data Preprocessing/23. Correlation Analysis.mp4 81MB
  9. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 81MB
  10. 6. Linear Regression/5. Simple Linear Regression in Python.mp4 79MB
  11. 5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 79MB
  12. 5. Data Preprocessing/7. EDD in Python.mp4 75MB
  13. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 74MB
  14. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 73MB
  15. 5. Data Preprocessing/24. Correlation Analysis in Python.mp4 68MB
  16. 3. Basics of Statistics/3. Describing data Graphically.mp4 65MB
  17. 6. Linear Regression/8. The F - statistic.mp4 64MB
  18. 6. Linear Regression/17. Test train split in Python.mp4 58MB
  19. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 56MB
  20. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 54MB
  21. 5. Data Preprocessing/17. Variable transformation and deletion in Python.mp4 53MB
  22. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 51MB
  23. 6. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 50MB
  24. 6. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 50MB
  25. 6. Linear Regression/14. Test-train split.mp4 49MB
  26. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 49MB
  27. 1. Introduction/2. Course contents.mp4 48MB
  28. 4. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 45MB
  29. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 41MB
  30. 6. Linear Regression/7. Multiple Linear Regression.mp4 39MB
  31. 6. Linear Regression/21. Shrinkage methods Ridge and Lasso.mp4 39MB
  32. 3. Basics of Statistics/4. Measures of Centers.mp4 39MB
  33. 5. Data Preprocessing/21. Dummy variable creation in Python.mp4 34MB
  34. 5. Data Preprocessing/4. Importing Data in Python.mp4 32MB
  35. 6. Linear Regression/15. Bias Variance trade-off.mp4 30MB
  36. 5. Data Preprocessing/13. Missing Value Imputation in Python.mp4 29MB
  37. 5. Data Preprocessing/9. Outlier Treatment.mp4 28MB
  38. 5. Data Preprocessing/12. Missing Value Imputation.mp4 28MB
  39. 5. Data Preprocessing/6. Univariate analysis and EDD.mp4 27MB
  40. 6. Linear Regression/10. Interpreting results of Categorical variables.mp4 27MB
  41. 5. Data Preprocessing/1. Gathering Business Knowledge.mp4 25MB
  42. 5. Data Preprocessing/19. Non-usable variables.mp4 24MB
  43. 5. Data Preprocessing/2. Data Exploration.mp4 23MB
  44. 3. Basics of Statistics/6. Measures of Dispersion.mp4 23MB
  45. 3. Basics of Statistics/1. Types of Data.mp4 22MB
  46. 5. Data Preprocessing/15. Seasonality in Data.mp4 21MB
  47. 1. Introduction/4. This is a milestone!.mp4 21MB
  48. 6. Linear Regression/19. Linear models other than OLS.mp4 19MB
  49. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 19MB
  50. 6. Linear Regression/23. Heteroscedasticity.mp4 18MB
  51. 1. Introduction/1. Welcome to the course!.mp4 16MB
  52. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 16MB
  53. 7. Bonus Section/1. The final milestone!.mp4 12MB
  54. 3. Basics of Statistics/2. Types of Statistics.mp4 11MB
  55. 6. Linear Regression/1. The Problem Statement.mp4 11MB
  56. 4. Introduction to Machine Learning/2.1 Lecture_machineLearning.pdf 1002KB
  57. 4. Introduction to Machine Learning/1.1 Lecture_machineLearning.pdf 992KB
  58. 1. Introduction/2.1 00_Introduction_01.pdf 801KB
  59. 3. Basics of Statistics/5.1 Exercise 1.pdf 554KB
  60. 3. Basics of Statistics/7.1 Exercise 2.pdf 470KB
  61. 5. Data Preprocessing/16.1 04_07_Variable_Transformation.pdf 423KB
  62. 5. Data Preprocessing/15.1 04_07_PDE_Seasonality.pdf 364KB
  63. 5. Data Preprocessing/9.1 04_06_PDE_Outlier_Treatment.pdf 355KB
  64. 5. Data Preprocessing/6.1 03_04_PDE_Univariate_Analysis_Uni.pdf 333KB
  65. 6. Linear Regression/3.1 05_03_Simple_lin_reg_Accuracy.pdf 333KB
  66. 6. Linear Regression/4.1 05_03_Simple_lin_reg_Accuracy.pdf 333KB
  67. 5. Data Preprocessing/3.1 03_03_PDE_Raw_Data_Analysis_Uni.pdf 332KB
  68. 6. Linear Regression/8.1 05_05_F_stat.pdf 328KB
  69. 3. Basics of Statistics/4.1 01_04_Lecture_Centers.pdf 323KB
  70. 5. Data Preprocessing/2.1 03_02_PDE_Data_exploration.pdf 323KB
  71. 3. Basics of Statistics/3.1 01_03_Lecture_DataSummaryandGraph.pdf 318KB
  72. 5. Data Preprocessing/12.1 04_05_PDE_Missing_value.pdf 316KB
  73. 6. Linear Regression/2.1 05_02_Simple_lin_reg.pdf 285KB
  74. 5. Data Preprocessing/23.1 04_10_Correlation.pdf 267KB
  75. 6. Linear Regression/1.1 05_01_Intro.pdf 239KB
  76. 6. Linear Regression/14.1 05_12_Test_Train.pdf 239KB
  77. 6. Linear Regression/7.1 05_04_Multiple_lin_reg.pdf 220KB
  78. 6. Linear Regression/15.1 05_13_Bias_Var_tradeoff.pdf 212KB
  79. 6. Linear Regression/20.1 05_10_Subset_Selection.pdf 209KB
  80. 6. Linear Regression/21.1 05_11_Shrinkage_methods.pdf 188KB
  81. 3. Basics of Statistics/1.1 01_01_Lecture_TypesOfData.pdf 178KB
  82. 3. Basics of Statistics/2.1 01_02_Lecture_TypesOfStatistics.pdf 172KB
  83. 5. Data Preprocessing/20.1 04_11_Dummy_Var.pdf 163KB
  84. 6. Linear Regression/19.1 05_09_Other_lin_model.pdf 157KB
  85. 6. Linear Regression/10.1 05_06_Cat_var.pdf 155KB
  86. 5. Data Preprocessing/1.1 03_01_PDE_Business_knowledge.pdf 154KB
  87. 5. Data Preprocessing/19.1 04_08_PDE_Non_Usable_var.pdf 138KB
  88. 2. Setting up Python and Jupyter Notebook/8.1 Customer.csv 64KB
  89. 5. Data Preprocessing/3.2 House_Price.csv 53KB
  90. 5. Data Preprocessing/4.1 House_Price.csv 53KB
  91. 5. Data Preprocessing/5.1 Movie_collection_train.csv 43KB
  92. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.srt 19KB
  93. 5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.srt 18KB
  94. 6. Linear Regression/22. Ridge regression and Lasso in Python.srt 18KB
  95. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 18KB
  96. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 17KB
  97. 6. Linear Regression/3. Assessing accuracy of predicted coefficients.srt 16KB
  98. 6. Linear Regression/20. Subset selection techniques.srt 13KB
  99. 6. Linear Regression/11. Multiple Linear Regression in Python.srt 13KB
  100. 3. Basics of Statistics/3. Describing data Graphically.srt 13KB
  101. 5. Data Preprocessing/10. Outlier Treatment in Python.srt 13KB
  102. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12KB
  103. 6. Linear Regression/25.1 Movie_collection_test.csv 12KB
  104. 5. Data Preprocessing/23. Correlation Analysis.srt 12KB
  105. 6. Linear Regression/5. Simple Linear Regression in Python.srt 12KB
  106. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 11KB
  107. 5. Data Preprocessing/7. EDD in Python.srt 10KB
  108. 6. Linear Regression/14. Test-train split.srt 10KB
  109. 4. Introduction to Machine Learning/2. Building a Machine Learning Model.srt 10KB
  110. 6. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10KB
  111. 1. Introduction/2. Course contents.srt 9KB
  112. 6. Linear Regression/8. The F - statistic.srt 9KB
  113. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9KB
  114. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 9KB
  115. 6. Linear Regression/21. Shrinkage methods Ridge and Lasso.srt 8KB
  116. 5. Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8KB
  117. 3. Basics of Statistics/4. Measures of Centers.srt 8KB
  118. 6. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8KB
  119. 6. Linear Regression/17. Test train split in Python.srt 8KB
  120. 5. Data Preprocessing/17. Variable transformation and deletion in Python.srt 7KB
  121. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 7KB
  122. 5. Data Preprocessing/24. Correlation Analysis in Python.srt 7KB
  123. 6. Linear Regression/15. Bias Variance trade-off.srt 7KB
  124. 6. Linear Regression/7. Multiple Linear Regression.srt 6KB
  125. 5. Data Preprocessing/19. Non-usable variables.srt 6KB
  126. 5. Data Preprocessing/4. Importing Data in Python.srt 6KB
  127. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 5KB
  128. 5. Data Preprocessing/21. Dummy variable creation in Python.srt 5KB
  129. 6. Linear Regression/10. Interpreting results of Categorical variables.srt 5KB
  130. 3. Basics of Statistics/6. Measures of Dispersion.srt 5KB
  131. 3. Basics of Statistics/1. Types of Data.srt 5KB
  132. 5. Data Preprocessing/9. Outlier Treatment.srt 5KB
  133. 6. Linear Regression/19. Linear models other than OLS.srt 4KB
  134. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4KB
  135. 5. Data Preprocessing/13. Missing Value Imputation in Python.srt 4KB
  136. 5. Data Preprocessing/12. Missing Value Imputation.srt 4KB
  137. 5. Data Preprocessing/1. Gathering Business Knowledge.srt 4KB
  138. 5. Data Preprocessing/15. Seasonality in Data.srt 4KB
  139. 1. Introduction/4. This is a milestone!.srt 4KB
  140. 5. Data Preprocessing/6. Univariate analysis and EDD.srt 4KB
  141. 5. Data Preprocessing/2. Data Exploration.srt 4KB
  142. 1. Introduction/1. Welcome to the course!.srt 3KB
  143. 3. Basics of Statistics/2. Types of Statistics.srt 3KB
  144. 6. Linear Regression/23. Heteroscedasticity.srt 3KB
  145. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 2KB
  146. 7. Bonus Section/2. Congratulations & About your certificate.html 2KB
  147. 7. Bonus Section/1. The final milestone!.srt 2KB
  148. 6. Linear Regression/1. The Problem Statement.srt 2KB
  149. 6. Linear Regression/16. More about test-train split.html 559B
  150. 5. Data Preprocessing/5. Project exercise 1.html 431B
  151. 6. Linear Regression/24. Project Exercise 10.html 424B
  152. 3. Basics of Statistics/5. Practice Exercise 1.html 357B
  153. 1. Introduction/3. Course Resources.html 336B
  154. 6. Linear Regression/13. Project Exercise 9.html 327B
  155. 6. Linear Regression/6. Project Exercise 8.html 322B
  156. 6. Linear Regression/25. Final Project Exercise.html 301B
  157. 3. Basics of Statistics/7. Practice Exercise 2.html 300B
  158. 5. Data Preprocessing/25. Project Exercise 7.html 288B
  159. 5. Data Preprocessing/18. Project Exercise 5.html 285B
  160. 5. Data Preprocessing/14. Project Exercise 4.html 238B
  161. 5. Data Preprocessing/11. Project Exercise 3.html 233B
  162. 4. Introduction to Machine Learning/3. Introduction to Machine learning quiz.html 210B
  163. 5. Data Preprocessing/26. Quiz.html 210B
  164. 6. Linear Regression/12. Quiz.html 210B
  165. 6. Linear Regression/18. Quiz.html 210B
  166. 6. Linear Regression/9. Quiz.html 210B
  167. 5. Data Preprocessing/22. Project Exercise 6.html 202B
  168. 5. Data Preprocessing/8. Project Exercise 2.html 177B
  169. 0. Websites you may like/[FCS Forum].url 133B
  170. 0. Websites you may like/[FreeCourseSite.com].url 127B
  171. 0. Websites you may like/[CourseClub.ME].url 122B
  172. 0. Websites you may like/[GigaCourse.Com].url 49B