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[] Udemy - Data Science with Python Complete Course

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  • 文件大小:8GB
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  • 最近下载:2021-11-24 07:24:19
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文件列表

  1. 5. Let's dig deeper/2. EDA on Mc'donalds Data Set.mp4 690MB
  2. 4. Advance Python/2. Advance Programming in Python Part 2.mp4 600MB
  3. 5. Let's dig deeper/3. Exploratory Data Analysis.mp4 439MB
  4. 4. Advance Python/1. Advance Programming in Python.mp4 360MB
  5. 4. Advance Python/5. Multivariate Plotting.mp4 336MB
  6. 7. Module Seven/2. How to use Linear Regression.mp4 304MB
  7. 3. Python for Data Science/15. Control Flow in Python.mp4 274MB
  8. 4. Advance Python/3. Data Visualisations.mp4 264MB
  9. 3. Python for Data Science/17. Types of Functions in Python.mp4 229MB
  10. 3. Python for Data Science/9. Data Types & Related Stuffs in Python.mp4 203MB
  11. 2. Basic Maths Required for Data Science/8. Probability.mp4 180MB
  12. 7. Module Seven/3. Logistic Regression.mp4 156MB
  13. 7. Module Seven/1. Linear Regression.mp4 150MB
  14. 2. Basic Maths Required for Data Science/4. Measures of Spread.mp4 149MB
  15. 4. Advance Python/4. Bivariate Plotting.mp4 149MB
  16. 7. Module Seven/4. Logistic Regression on Titanic Data Set.mp4 141MB
  17. 10. Project Telecom Churn Production/3. Project Part 3.mp4 129MB
  18. 2. Basic Maths Required for Data Science/1. Let's Start with Statistics.mp4 129MB
  19. 3. Python for Data Science/14. Operators in Python.mp4 121MB
  20. 10. Project Telecom Churn Production/5. Project Let's Finalise it.mp4 119MB
  21. 3. Python for Data Science/16. Functions in Python.mp4 117MB
  22. 10. Project Telecom Churn Production/4. Project part 4.mp4 114MB
  23. 10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.mp4 113MB
  24. 2. Basic Maths Required for Data Science/11. Normal Probability Distribution.mp4 104MB
  25. 2. Basic Maths Required for Data Science/3. Types of Statistics.mp4 92MB
  26. 7. Module Seven/24. Model Evaluation recall Curve.mp4 90MB
  27. 3. Python for Data Science/18. Argument in a Function.mp4 88MB
  28. 2. Basic Maths Required for Data Science/6. Plots Visualisation.mp4 87MB
  29. 7. Module Seven/6. Algorithms used in Decision Treee.mp4 87MB
  30. 7. Module Seven/10. Working on Titanic Data Set.mp4 85MB
  31. 9. Featured Topics in Java/1. Big Data.mp4 85MB
  32. 2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.mp4 84MB
  33. 6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.mp4 81MB
  34. 3. Python for Data Science/19. Recursive Functions in Python.mp4 78MB
  35. 7. Module Seven/18. Linear Regression.mp4 76MB
  36. 3. Python for Data Science/12. Output Formatting.mp4 76MB
  37. 7. Module Seven/15. Random Forest Implementation on Titanic Data Set.mp4 74MB
  38. 1. Introduction/1. Getting Started with Data Science.mp4 72MB
  39. 2. Basic Maths Required for Data Science/2. Data Quality Issues.mp4 63MB
  40. 8. Module Eight/3. All about R Language.mp4 58MB
  41. 9. Featured Topics in Java/2. Intro to Hadoop.mp4 58MB
  42. 2. Basic Maths Required for Data Science/5. Measures of Shapes.mp4 58MB
  43. 10. Project Telecom Churn Production/2. Project part 2.mp4 58MB
  44. 2. Basic Maths Required for Data Science/10. Random Variables.mp4 57MB
  45. 9. Featured Topics in Java/3. Intro to Tableu.mp4 57MB
  46. 3. Python for Data Science/10. Conversion of Data Types in Python.mp4 54MB
  47. 9. Featured Topics in Java/4. Intro to Business Analytics.mp4 54MB
  48. 3. Python for Data Science/1. Python for Data Science.mp4 52MB
  49. 2. Basic Maths Required for Data Science/12. Central Limit Theorem.mp4 49MB
  50. 3. Python for Data Science/20. Lambda or Anonymous Functions in Python.mp4 47MB
  51. 2. Basic Maths Required for Data Science/7. Inferential Statistics.mp4 46MB
  52. 3. Python for Data Science/8. Variables in Python.mp4 44MB
  53. 6. Let's Explore in to Machine Learning/3. Reinforement Learning.mp4 43MB
  54. 3. Python for Data Science/5. Comments in Python.mp4 42MB
  55. 7. Module Seven/7. Gini Index.mp4 39MB
  56. 3. Python for Data Science/6. Python Indentation.mp4 38MB
  57. 7. Module Seven/20. Confusion Matrix.mp4 37MB
  58. 7. Module Seven/8. Issues with Decision Tree.mp4 37MB
  59. 7. Module Seven/23. AUC ROC curve.mp4 36MB
  60. 7. Module Seven/22. FB score.mp4 36MB
  61. 3. Python for Data Science/2. Python Installation - Google Collab.mp4 36MB
  62. 3. Python for Data Science/4. Identifiers in Python.mp4 36MB
  63. 3. Python for Data Science/13. User Input in Python.mp4 32MB
  64. 7. Module Seven/16. Model Evaluation Technique.mp4 31MB
  65. 6. Let's Explore in to Machine Learning/2. Unsupervised Learning.mp4 29MB
  66. 5. Let's dig deeper/1. EDA.mp4 27MB
  67. 7. Module Seven/5. Decision Tree.mp4 27MB
  68. 7. Module Seven/17. Concept of R-Squared.mp4 27MB
  69. 2. Basic Maths Required for Data Science/9. Conditional Probability.mp4 26MB
  70. 7. Module Seven/11. Random Forest.mp4 26MB
  71. 7. Module Seven/21. Recall Sensitivity True Rate of Positive.mp4 23MB
  72. 8. Module Eight/2. Data Analysis using R part 2.mp4 23MB
  73. 7. Module Seven/19. Classification.mp4 22MB
  74. 3. Python for Data Science/3. Python Basics.mp4 20MB
  75. 8. Module Eight/1. Data Analysis using R.mp4 20MB
  76. 7. Module Seven/14. Application of Random Forest.mp4 18MB
  77. 3. Python for Data Science/7. Python Statements.mp4 18MB
  78. 3. Python for Data Science/11. Python IO functions.mp4 14MB
  79. 7. Module Seven/9. Applications of Decision Tree.mp4 13MB
  80. 7. Module Seven/13. Why Random Forest.mp4 10MB
  81. 7. Module Seven/12. Types of Random Forest.mp4 4MB
  82. 4. Advance Python/2. Advance Programming in Python Part 2.srt 121KB
  83. 5. Let's dig deeper/2. EDA on Mc'donalds Data Set.srt 112KB
  84. 5. Let's dig deeper/3. Exploratory Data Analysis.srt 78KB
  85. 4. Advance Python/1. Advance Programming in Python.srt 73KB
  86. 4. Advance Python/5. Multivariate Plotting.srt 61KB
  87. 7. Module Seven/2. How to use Linear Regression.srt 57KB
  88. 3. Python for Data Science/15. Control Flow in Python.srt 54KB
  89. 4. Advance Python/3. Data Visualisations.srt 45KB
  90. 7. Module Seven/3. Logistic Regression.srt 40KB
  91. 7. Module Seven/1. Linear Regression.srt 37KB
  92. 4. Advance Python/4. Bivariate Plotting.srt 37KB
  93. 3. Python for Data Science/17. Types of Functions in Python.srt 33KB
  94. 3. Python for Data Science/9. Data Types & Related Stuffs in Python.srt 33KB
  95. 10. Project Telecom Churn Production/3. Project Part 3.srt 24KB
  96. 7. Module Seven/6. Algorithms used in Decision Treee.srt 24KB
  97. 3. Python for Data Science/14. Operators in Python.srt 23KB
  98. 7. Module Seven/4. Logistic Regression on Titanic Data Set.srt 22KB
  99. 10. Project Telecom Churn Production/4. Project part 4.srt 22KB
  100. 10. Project Telecom Churn Production/5. Project Let's Finalise it.srt 22KB
  101. 3. Python for Data Science/16. Functions in Python.srt 20KB
  102. 6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.srt 20KB
  103. 10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.srt 18KB
  104. 7. Module Seven/24. Model Evaluation recall Curve.srt 15KB
  105. 9. Featured Topics in Java/1. Big Data.srt 15KB
  106. 1. Introduction/1. Getting Started with Data Science.srt 14KB
  107. 7. Module Seven/10. Working on Titanic Data Set.srt 14KB
  108. 2. Basic Maths Required for Data Science/8. Probability.srt 13KB
  109. 2. Basic Maths Required for Data Science/1. Let's Start with Statistics.srt 13KB
  110. 9. Featured Topics in Java/3. Intro to Tableu.srt 12KB
  111. 7. Module Seven/15. Random Forest Implementation on Titanic Data Set.srt 12KB
  112. 3. Python for Data Science/19. Recursive Functions in Python.srt 12KB
  113. 2. Basic Maths Required for Data Science/4. Measures of Spread.srt 12KB
  114. 3. Python for Data Science/18. Argument in a Function.srt 12KB
  115. 9. Featured Topics in Java/4. Intro to Business Analytics.srt 12KB
  116. 7. Module Seven/7. Gini Index.srt 12KB
  117. 8. Module Eight/3. All about R Language.srt 12KB
  118. 7. Module Seven/18. Linear Regression.srt 12KB
  119. 2. Basic Maths Required for Data Science/11. Normal Probability Distribution.srt 11KB
  120. 7. Module Seven/22. FB score.srt 11KB
  121. 9. Featured Topics in Java/2. Intro to Hadoop.srt 11KB
  122. 10. Project Telecom Churn Production/2. Project part 2.srt 10KB
  123. 5. Let's dig deeper/1. EDA.srt 10KB
  124. 2. Basic Maths Required for Data Science/6. Plots Visualisation.srt 10KB
  125. 2. Basic Maths Required for Data Science/3. Types of Statistics.srt 9KB
  126. 6. Let's Explore in to Machine Learning/3. Reinforement Learning.srt 9KB
  127. 2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.srt 9KB
  128. 7. Module Seven/20. Confusion Matrix.srt 9KB
  129. 7. Module Seven/8. Issues with Decision Tree.srt 9KB
  130. 7. Module Seven/23. AUC ROC curve.srt 8KB
  131. 7. Module Seven/16. Model Evaluation Technique.srt 8KB
  132. 3. Python for Data Science/2. Python Installation - Google Collab.srt 8KB
  133. 3. Python for Data Science/10. Conversion of Data Types in Python.srt 7KB
  134. 7. Module Seven/5. Decision Tree.srt 7KB
  135. 3. Python for Data Science/20. Lambda or Anonymous Functions in Python.srt 7KB
  136. 6. Let's Explore in to Machine Learning/2. Unsupervised Learning.srt 7KB
  137. 2. Basic Maths Required for Data Science/5. Measures of Shapes.srt 7KB
  138. 3. Python for Data Science/8. Variables in Python.srt 6KB
  139. 3. Python for Data Science/5. Comments in Python.srt 6KB
  140. 3. Python for Data Science/12. Output Formatting.srt 6KB
  141. 3. Python for Data Science/6. Python Indentation.srt 6KB
  142. 2. Basic Maths Required for Data Science/10. Random Variables.srt 6KB
  143. 2. Basic Maths Required for Data Science/2. Data Quality Issues.srt 6KB
  144. 7. Module Seven/11. Random Forest.srt 6KB
  145. 7. Module Seven/19. Classification.srt 6KB
  146. 7. Module Seven/17. Concept of R-Squared.srt 6KB
  147. 2. Basic Maths Required for Data Science/7. Inferential Statistics.srt 5KB
  148. 7. Module Seven/21. Recall Sensitivity True Rate of Positive.srt 5KB
  149. 3. Python for Data Science/1. Python for Data Science.srt 5KB
  150. 8. Module Eight/2. Data Analysis using R part 2.srt 5KB
  151. 2. Basic Maths Required for Data Science/12. Central Limit Theorem.srt 5KB
  152. 8. Module Eight/1. Data Analysis using R.srt 4KB
  153. 3. Python for Data Science/4. Identifiers in Python.srt 4KB
  154. 3. Python for Data Science/7. Python Statements.srt 4KB
  155. 3. Python for Data Science/3. Python Basics.srt 3KB
  156. 7. Module Seven/14. Application of Random Forest.srt 3KB
  157. 2. Basic Maths Required for Data Science/9. Conditional Probability.srt 3KB
  158. 7. Module Seven/9. Applications of Decision Tree.srt 3KB
  159. 3. Python for Data Science/13. User Input in Python.srt 3KB
  160. 7. Module Seven/13. Why Random Forest.srt 2KB
  161. 3. Python for Data Science/11. Python IO functions.srt 2KB
  162. 7. Module Seven/12. Types of Random Forest.srt 1KB
  163. 0. Websites you may like/[FCS Forum].url 133B
  164. 0. Websites you may like/[FreeCourseSite.com].url 127B
  165. 0. Websites you may like/[CourseClub.ME].url 122B
  166. 0. Websites you may like/[GigaCourse.Com].url 49B