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[] Udemy - 2021 Python for Machine Learning & Data Science Masterclass

  • 收录时间:2021-06-28 00:27:16
  • 文件大小:6GB
  • 下载次数:1
  • 最近下载:2021-06-28 00:27:15
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文件列表

  1. 5. Pandas/28. Pandas Project Exercise Solutions.mp4 182MB
  2. 8. Data Analysis and Visualization Capstone Project Exercise/4. Capstone Project Solutions - Part Three.mp4 144MB
  3. 5. Pandas/26. Pandas Pivot Tables.mp4 129MB
  4. 7. Seaborn Data Visualizations/2. Scatterplots with Seaborn.mp4 129MB
  5. 6. Matplotlib/11. Matplotlib Exercise Questions - Solutions.mp4 123MB
  6. 8. Data Analysis and Visualization Capstone Project Exercise/2. Capstone Project Solutions - Part One.mp4 117MB
  7. 5. Pandas/4. DataFrames - Part One - Creating a DataFrame.mp4 114MB
  8. 7. Seaborn Data Visualizations/8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 111MB
  9. 8. Data Analysis and Visualization Capstone Project Exercise/3. Capstone Project Solutions - Part Two.mp4 111MB
  10. 7. Seaborn Data Visualizations/14. Seaborn Plot Exercises Solutions.mp4 111MB
  11. 4. NumPy/2. NumPy Arrays.mp4 110MB
  12. 5. Pandas/23. Pandas Input and Output - HTML Tables.mp4 107MB
  13. 5. Pandas/15. GroupBy Operations - Part Two - MultiIndex.mp4 106MB
  14. 5. Pandas/25. Pandas Input and Output - SQL Databases.mp4 103MB
  15. 5. Pandas/21. Pandas - Time Methods for Date and Time Data.mp4 102MB
  16. 10. Linear Regression/24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 100MB
  17. 1. Introduction to Course/3. Anaconda Python and Jupyter Install and Setup.mp4 99MB
  18. 5. Pandas/10. Pandas - Useful Methods - Apply on Multiple Columns.mp4 99MB
  19. 5. Pandas/13. Missing Data - Pandas Operations.mp4 98MB
  20. 5. Pandas/7. DataFrames - Part Four - Working with Rows.mp4 97MB
  21. 10. Linear Regression/23. L2 Regularization - Ridge Regression - Python Implementation.mp4 96MB
  22. 6. Matplotlib/6. Matplotlib - Subplots Functionality.mp4 96MB
  23. 10. Linear Regression/25. L1 and L2 Regularization - Elastic Net.mp4 93MB
  24. 8. Data Analysis and Visualization Capstone Project Exercise/1. Capstone Project Overview.mp4 93MB
  25. 5. Pandas/14. GroupBy Operations - Part One.mp4 93MB
  26. 10. Linear Regression/6. Python coding Simple Linear Regression.mp4 92MB
  27. 7. Seaborn Data Visualizations/11. Seaborn Grid Plots.mp4 92MB
  28. 5. Pandas/8. Pandas - Conditional Filtering.mp4 90MB
  29. 5. Pandas/6. DataFrames - Part Three - Working with Columns.mp4 89MB
  30. 10. Linear Regression/11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 88MB
  31. 10. Linear Regression/3. Linear Regression - Understanding Ordinary Least Squares.mp4 86MB
  32. 5. Pandas/11. Pandas - Useful Methods - Statistical Information and Sorting.mp4 86MB
  33. 10. Linear Regression/8. Linear Regression - Scikit-Learn Train Test Split.mp4 83MB
  34. 6. Matplotlib/8. Matplotlib Styling - Colors and Styles.mp4 81MB
  35. 7. Seaborn Data Visualizations/4. Distribution Plots - Part Two - Coding with Seaborn.mp4 78MB
  36. 5. Pandas/20. Pandas - Text Methods for String Data.mp4 76MB
  37. 10. Linear Regression/9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 74MB
  38. 5. Pandas/9. Pandas - Useful Methods - Apply on Single Column.mp4 73MB
  39. 10. Linear Regression/16. Polynomial Regression - Choosing Degree of Polynomial.mp4 73MB
  40. 9. Machine Learning Concepts Overview/4. Supervised Machine Learning Process.mp4 71MB
  41. 7. Seaborn Data Visualizations/12. Seaborn - Matrix Plots.mp4 71MB
  42. 7. Seaborn Data Visualizations/10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 70MB
  43. 10. Linear Regression/5. Linear Regression - Gradient Descent.mp4 65MB
  44. 10. Linear Regression/20. Introduction to Cross Validation.mp4 63MB
  45. 7. Seaborn Data Visualizations/7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 61MB
  46. 10. Linear Regression/22. L2 Regularization - Ridge Regression Theory.mp4 61MB
  47. 10. Linear Regression/10. Linear Regression - Residual Plots.mp4 60MB
  48. 6. Matplotlib/4. Matplotlib - Implementing Figures and Axes.mp4 59MB
  49. 7. Seaborn Data Visualizations/6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 55MB
  50. 10. Linear Regression/2. Linear Regression - Algorithm History.mp4 55MB
  51. 10. Linear Regression/19. Feature Scaling.mp4 54MB
  52. 5. Pandas/5. DataFrames - Part Two - Basic Properties.mp4 54MB
  53. 6. Matplotlib/2. Matplotlib Basics.mp4 54MB
  54. 5. Pandas/17. Combining DataFrames - Inner Merge.mp4 54MB
  55. 5. Pandas/12. Missing Data - Overview.mp4 53MB
  56. 10. Linear Regression/13. Polynomial Regression - Creating Polynomial Features.mp4 53MB
  57. 6. Matplotlib/10. Matplotlib Exercise Questions Overview.mp4 51MB
  58. 5. Pandas/16. Combining DataFrames - Concatenation.mp4 51MB
  59. 7. Seaborn Data Visualizations/13. Seaborn Plot Exercises Overview.mp4 50MB
  60. 5. Pandas/22. Pandas Input and Output - CSV Files.mp4 50MB
  61. 1. Introduction to Course/4. Environment Setup.mp4 49MB
  62. 10. Linear Regression/14. Polynomial Regression - Training and Evaluation.mp4 49MB
  63. 4. NumPy/5. NumPy Operations.mp4 49MB
  64. 4. NumPy/7. Numpy Exercises - Solutions.mp4 49MB
  65. 4. NumPy/4. NumPy Indexing and Selection.mp4 46MB
  66. 10. Linear Regression/7. Overview of Scikit-Learn and Python.mp4 46MB
  67. 5. Pandas/3. Series - Part Two.mp4 45MB
  68. 9. Machine Learning Concepts Overview/2. Why Machine Learning.mp4 45MB
  69. 10. Linear Regression/12. Polynomial Regression - Theory and Motivation.mp4 44MB
  70. 10. Linear Regression/15. Bias Variance Trade-Off.mp4 43MB
  71. 5. Pandas/27. Pandas Project Exercise Overview.mp4 41MB
  72. 3. Machine Learning Pathway Overview/1. Machine Learning Pathway.mp4 41MB
  73. 6. Matplotlib/9. Advanced Matplotlib Commands (Optional).mp4 40MB
  74. 5. Pandas/19. Combining DataFrames - Outer Merge.mp4 40MB
  75. 10. Linear Regression/26. Linear Regression Project - Data Overview.mp4 39MB
  76. 9. Machine Learning Concepts Overview/3. Types of Machine Learning Algorithms.mp4 39MB
  77. 5. Pandas/2. Series - Part One.mp4 38MB
  78. 10. Linear Regression/4. Linear Regression - Cost Functions.mp4 36MB
  79. 5. Pandas/24. Pandas Input and Output - Excel Files.mp4 35MB
  80. 10. Linear Regression/21. Regularization Data Setup.mp4 34MB
  81. 6. Matplotlib/7. Matplotlib Styling - Legends.mp4 34MB
  82. 10. Linear Regression/18. Regularization Overview.mp4 33MB
  83. 7. Seaborn Data Visualizations/3. Distribution Plots - Part One - Understanding Plot Types.mp4 32MB
  84. 9. Machine Learning Concepts Overview/1. Introduction to Machine Learning Overview Section.mp4 30MB
  85. 2. OPTIONAL Python Crash Course/2. Python Crash Course - Part One.mp4 30MB
  86. 10. Linear Regression/17. Polynomial Regression - Model Deployment.mp4 29MB
  87. 5. Pandas/18. Combining DataFrames - Left and Right Merge.mp4 28MB
  88. 1. Introduction to Course/3.1 UNZIP_ME_FOR_NOTEBOOKS.zip 27MB
  89. 1. Introduction to Course/2.1 UNZIP_ME_FOR_NOTEBOOKS.zip 27MB
  90. 6. Matplotlib/3. Matplotlib - Understanding the Figure Object.mp4 26MB
  91. 2. OPTIONAL Python Crash Course/6. Python Crash Course - Exercise Solutions.mp4 25MB
  92. 1. Introduction to Course/2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 25MB
  93. 6. Matplotlib/5. Matplotlib - Figure Parameters.mp4 24MB
  94. 7. Seaborn Data Visualizations/9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 23MB
  95. 2. OPTIONAL Python Crash Course/4. Python Crash Course - Part Three.mp4 23MB
  96. 2. OPTIONAL Python Crash Course/3. Python Crash Course - Part Two.mp4 22MB
  97. 7. Seaborn Data Visualizations/5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 22MB
  98. 6. Matplotlib/1. Introduction to Matplotlib.mp4 22MB
  99. 5. Pandas/1. Introduction to Pandas.mp4 21MB
  100. 7. Seaborn Data Visualizations/1. Introduction to Seaborn.mp4 20MB
  101. 9. Machine Learning Concepts Overview/5. Companion Book - Introduction to Statistical Learning.mp4 19MB
  102. 4. NumPy/6. NumPy Exercises.mp4 12MB
  103. 4. NumPy/1. Introduction to NumPy.mp4 11MB
  104. 10. Linear Regression/1. Introduction to Linear Regression Section.mp4 9MB
  105. 2. OPTIONAL Python Crash Course/5. Python Crash Course - Exercise Questions.mp4 5MB
  106. 5. Pandas/28. Pandas Project Exercise Solutions.srt 39KB
  107. 5. Pandas/26. Pandas Pivot Tables.srt 32KB
  108. 4. NumPy/2. NumPy Arrays.srt 32KB
  109. 5. Pandas/21. Pandas - Time Methods for Date and Time Data.srt 32KB
  110. 8. Data Analysis and Visualization Capstone Project Exercise/4. Capstone Project Solutions - Part Three.srt 31KB
  111. 7. Seaborn Data Visualizations/2. Scatterplots with Seaborn.srt 30KB
  112. 5. Pandas/25. Pandas Input and Output - SQL Databases.srt 29KB
  113. 5. Pandas/4. DataFrames - Part One - Creating a DataFrame.srt 29KB
  114. 6. Matplotlib/6. Matplotlib - Subplots Functionality.srt 29KB
  115. 7. Seaborn Data Visualizations/8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt 28KB
  116. 10. Linear Regression/6. Python coding Simple Linear Regression.srt 28KB
  117. 5. Pandas/13. Missing Data - Pandas Operations.srt 27KB
  118. 5. Pandas/8. Pandas - Conditional Filtering.srt 27KB
  119. 8. Data Analysis and Visualization Capstone Project Exercise/2. Capstone Project Solutions - Part One.srt 27KB
  120. 10. Linear Regression/23. L2 Regularization - Ridge Regression - Python Implementation.srt 26KB
  121. 5. Pandas/10. Pandas - Useful Methods - Apply on Multiple Columns.srt 26KB
  122. 10. Linear Regression/25. L1 and L2 Regularization - Elastic Net.srt 26KB
  123. 10. Linear Regression/11. Linear Regression - Model Deployment and Coefficient Interpretation.srt 26KB
  124. 7. Seaborn Data Visualizations/4. Distribution Plots - Part Two - Coding with Seaborn.srt 25KB
  125. 2. OPTIONAL Python Crash Course/2. Python Crash Course - Part One.srt 25KB
  126. 6. Matplotlib/11. Matplotlib Exercise Questions - Solutions.srt 25KB
  127. 5. Pandas/20. Pandas - Text Methods for String Data.srt 24KB
  128. 10. Linear Regression/8. Linear Regression - Scikit-Learn Train Test Split.srt 24KB
  129. 8. Data Analysis and Visualization Capstone Project Exercise/3. Capstone Project Solutions - Part Two.srt 23KB
  130. 5. Pandas/11. Pandas - Useful Methods - Statistical Information and Sorting.srt 23KB
  131. 10. Linear Regression/9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt 23KB
  132. 10. Linear Regression/3. Linear Regression - Understanding Ordinary Least Squares.srt 23KB
  133. 10. Linear Regression/24. L1 Regularization - Lasso Regression - Background and Implementation.srt 22KB
  134. 7. Seaborn Data Visualizations/14. Seaborn Plot Exercises Solutions.srt 22KB
  135. 5. Pandas/23. Pandas Input and Output - HTML Tables.srt 22KB
  136. 1. Introduction to Course/3. Anaconda Python and Jupyter Install and Setup.srt 22KB
  137. 5. Pandas/14. GroupBy Operations - Part One.srt 21KB
  138. 7. Seaborn Data Visualizations/12. Seaborn - Matrix Plots.srt 21KB
  139. 5. Pandas/7. DataFrames - Part Four - Working with Rows.srt 21KB
  140. 6. Matplotlib/8. Matplotlib Styling - Colors and Styles.srt 21KB
  141. 6. Matplotlib/4. Matplotlib - Implementing Figures and Axes.srt 21KB
  142. 5. Pandas/15. GroupBy Operations - Part Two - MultiIndex.srt 21KB
  143. 10. Linear Regression/22. L2 Regularization - Ridge Regression Theory.srt 21KB
  144. 5. Pandas/6. DataFrames - Part Three - Working with Columns.srt 21KB
  145. 8. Data Analysis and Visualization Capstone Project Exercise/1. Capstone Project Overview.srt 21KB
  146. 7. Seaborn Data Visualizations/11. Seaborn Grid Plots.srt 20KB
  147. 5. Pandas/9. Pandas - Useful Methods - Apply on Single Column.srt 20KB
  148. 10. Linear Regression/10. Linear Regression - Residual Plots.srt 20KB
  149. 7. Seaborn Data Visualizations/7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt 20KB
  150. 10. Linear Regression/16. Polynomial Regression - Choosing Degree of Polynomial.srt 20KB
  151. 10. Linear Regression/20. Introduction to Cross Validation.srt 20KB
  152. 9. Machine Learning Concepts Overview/4. Supervised Machine Learning Process.srt 20KB
  153. 6. Matplotlib/2. Matplotlib Basics.srt 20KB
  154. 5. Pandas/17. Combining DataFrames - Inner Merge.srt 19KB
  155. 5. Pandas/12. Missing Data - Overview.srt 18KB
  156. 2. OPTIONAL Python Crash Course/3. Python Crash Course - Part Two.srt 18KB
  157. 10. Linear Regression/5. Linear Regression - Gradient Descent.srt 17KB
  158. 5. Pandas/22. Pandas Input and Output - CSV Files.srt 17KB
  159. 2. OPTIONAL Python Crash Course/4. Python Crash Course - Part Three.srt 17KB
  160. 10. Linear Regression/13. Polynomial Regression - Creating Polynomial Features.srt 16KB
  161. 4. NumPy/4. NumPy Indexing and Selection.srt 16KB
  162. 10. Linear Regression/15. Bias Variance Trade-Off.srt 16KB
  163. 3. Machine Learning Pathway Overview/1. Machine Learning Pathway.srt 16KB
  164. 7. Seaborn Data Visualizations/10. Seaborn - Comparison Plots - Coding with Seaborn.srt 16KB
  165. 5. Pandas/3. Series - Part Two.srt 15KB
  166. 5. Pandas/16. Combining DataFrames - Concatenation.srt 15KB
  167. 7. Seaborn Data Visualizations/3. Distribution Plots - Part One - Understanding Plot Types.srt 15KB
  168. 10. Linear Regression/19. Feature Scaling.srt 15KB
  169. 9. Machine Learning Concepts Overview/2. Why Machine Learning.srt 15KB
  170. 7. Seaborn Data Visualizations/6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt 15KB
  171. 5. Pandas/19. Combining DataFrames - Outer Merge.srt 15KB
  172. 1. Introduction to Course/4. Environment Setup.srt 14KB
  173. 10. Linear Regression/14. Polynomial Regression - Training and Evaluation.srt 14KB
  174. 2. OPTIONAL Python Crash Course/6. Python Crash Course - Exercise Solutions.srt 13KB
  175. 5. Pandas/2. Series - Part One.srt 13KB
  176. 5. Pandas/5. DataFrames - Part Two - Basic Properties.srt 13KB
  177. 10. Linear Regression/2. Linear Regression - Algorithm History.srt 13KB
  178. 10. Linear Regression/21. Regularization Data Setup.srt 12KB
  179. 10. Linear Regression/7. Overview of Scikit-Learn and Python.srt 12KB
  180. 4. NumPy/5. NumPy Operations.srt 12KB
  181. 9. Machine Learning Concepts Overview/3. Types of Machine Learning Algorithms.srt 12KB
  182. 6. Matplotlib/3. Matplotlib - Understanding the Figure Object.srt 12KB
  183. 10. Linear Regression/4. Linear Regression - Cost Functions.srt 11KB
  184. 7. Seaborn Data Visualizations/13. Seaborn Plot Exercises Overview.srt 11KB
  185. 10. Linear Regression/12. Polynomial Regression - Theory and Motivation.srt 11KB
  186. 5. Pandas/24. Pandas Input and Output - Excel Files.srt 11KB
  187. 4. NumPy/7. Numpy Exercises - Solutions.srt 11KB
  188. 6. Matplotlib/7. Matplotlib Styling - Legends.srt 10KB
  189. 10. Linear Regression/18. Regularization Overview.srt 10KB
  190. 5. Pandas/27. Pandas Project Exercise Overview.srt 10KB
  191. 6. Matplotlib/10. Matplotlib Exercise Questions Overview.srt 9KB
  192. 5. Pandas/18. Combining DataFrames - Left and Right Merge.srt 9KB
  193. 7. Seaborn Data Visualizations/5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt 9KB
  194. 7. Seaborn Data Visualizations/9. Seaborn - Comparison Plots - Understanding the Plot Types.srt 9KB
  195. 9. Machine Learning Concepts Overview/1. Introduction to Machine Learning Overview Section.srt 9KB
  196. 10. Linear Regression/17. Polynomial Regression - Model Deployment.srt 8KB
  197. 10. Linear Regression/26. Linear Regression Project - Data Overview.srt 8KB
  198. 6. Matplotlib/5. Matplotlib - Figure Parameters.srt 8KB
  199. 5. Pandas/1. Introduction to Pandas.srt 7KB
  200. 1. Introduction to Course/2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt 7KB
  201. 6. Matplotlib/1. Introduction to Matplotlib.srt 7KB
  202. 7. Seaborn Data Visualizations/1. Introduction to Seaborn.srt 7KB
  203. 6. Matplotlib/9. Advanced Matplotlib Commands (Optional).srt 6KB
  204. 9. Machine Learning Concepts Overview/5. Companion Book - Introduction to Statistical Learning.srt 5KB
  205. 4. NumPy/1. Introduction to NumPy.srt 3KB
  206. 10. Linear Regression/1. Introduction to Linear Regression Section.srt 3KB
  207. 2. OPTIONAL Python Crash Course/5. Python Crash Course - Exercise Questions.srt 3KB
  208. 4. NumPy/6. NumPy Exercises.srt 2KB
  209. 1. Introduction to Course/1. EARLY BIRD INFO.html 550B
  210. 2. OPTIONAL Python Crash Course/1. OPTIONAL Python Crash Course.html 472B
  211. 1. Introduction to Course/4.2 requirements.txt 221B
  212. 4. NumPy/3. Coding Exercise Check-in Creating NumPy Arrays.html 163B
  213. 1. Introduction to Course/4.1 Backup Google Link for requirements.txt file.html 143B
  214. [FreeCourseSite.com].url 127B