589689.xyz

[] Udemy - Python for Machine Learning The Complete Beginner's Course

  • 收录时间:2023-08-23 12:41:59
  • 文件大小:685MB
  • 下载次数:1
  • 最近下载:2023-08-23 12:41:59
  • 磁力链接:
立即下载 复制链接

文件列表

  1. 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp4 29MB
  2. 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp4 20MB
  3. 8. Recommender System/6. Sorting by title and rating.mp4 19MB
  4. 7. Clustering/6. Implementation in python.mp4 19MB
  5. 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp4 18MB
  6. 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp4 17MB
  7. 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp4 14MB
  8. 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp4 13MB
  9. 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp4 13MB
  10. 8. Recommender System/13. Correlation between the most-rated movies.mp4 13MB
  11. 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp4 13MB
  12. 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp4 13MB
  13. 7. Clustering/14. 3D Visualization of the predicted values.mp4 13MB
  14. 7. Clustering/10. Importing the dataset.mp4 13MB
  15. 8. Recommender System/17. Repeating the process for another movie.mp4 13MB
  16. 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp4 13MB
  17. 7. Clustering/11. Visualizing the dataset.mp4 12MB
  18. 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp4 12MB
  19. 8. Recommender System/10. Data pre-processing.mp4 11MB
  20. 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp4 11MB
  21. 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp4 10MB
  22. 8. Recommender System/4. Implementation in python Importing libraries & datasets.mp4 10MB
  23. 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp4 10MB
  24. 7. Clustering/15. Number of predicted clusters.mp4 9MB
  25. 2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp4 9MB
  26. 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp4 9MB
  27. 2. Simple Linear Regression/1. Introduction to regression.mp4 9MB
  28. 8. Recommender System/11. Sorting the most-rated movies.mp4 9MB
  29. 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp4 9MB
  30. 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp4 9MB
  31. 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp4 8MB
  32. 7. Clustering/13. 3D Visualization of the clusters.mp4 8MB
  33. 7. Clustering/8. Density-based clustering.mp4 8MB
  34. 2. Simple Linear Regression/2. How Does Linear Regression Work.mp4 8MB
  35. 7. Clustering/12. Defining the classifier.mp4 8MB
  36. 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp4 8MB
  37. 8. Recommender System/1. Introduction.mp4 8MB
  38. 1. Introduction to Machine Learning/1. What is Machine Learning.mp4 7MB
  39. 7. Clustering/7. Hierarchical clustering.mp4 7MB
  40. 8. Recommender System/9. Jointplot of the ratings and number of ratings.mp4 7MB
  41. 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp4 7MB
  42. 7. Clustering/4. Elbow method.mp4 7MB
  43. 8. Recommender System/16. Sorting values.mp4 7MB
  44. 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp4 7MB
  45. 7. Clustering/3. K-Means Clustering Algorithm.mp4 7MB
  46. 6. Classification Algorithms Logistic regression/1. Introduction.mp4 7MB
  47. 1. Introduction to Machine Learning/2. Applications of Machine Learning.mp4 7MB
  48. 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp4 6MB
  49. 5. Classification Algorithms Decision Tree/4. Decision tree structure.mp4 6MB
  50. 3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp4 6MB
  51. 1. Introduction to Machine Learning/4. What is Supervised learning.mp4 6MB
  52. 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp4 6MB
  53. 8. Recommender System/14. Sorting the data by correlation.mp4 6MB
  54. 8. Recommender System/8. Frequency distribution.mp4 6MB
  55. 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp4 6MB
  56. 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp4 6MB
  57. 5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp4 6MB
  58. 1. Introduction to Machine Learning/5. What is Unsupervised learning.mp4 6MB
  59. 7. Clustering/5. Steps of the Elbow method.mp4 6MB
  60. 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp4 6MB
  61. 8. Recommender System/7. Histogram showing number of ratings.mp4 6MB
  62. 6. Classification Algorithms Logistic regression/2. Implementation steps.mp4 5MB
  63. 8. Recommender System/12. Grabbing the ratings for two movies.mp4 5MB
  64. 2. Simple Linear Regression/3. Line representation.mp4 5MB
  65. 5. Classification Algorithms Decision Tree/2. What is Entropy.mp4 5MB
  66. 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp4 5MB
  67. 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp4 5MB
  68. 8. Recommender System/3. Content-based Recommender System.mp4 5MB
  69. 8. Recommender System/15. Filtering out movies.mp4 5MB
  70. 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp4 5MB
  71. 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp4 5MB
  72. 7. Clustering/1. Introduction to clustering.mp4 4MB
  73. 8. Recommender System/5. Merging datasets into one dataframe.mp4 4MB
  74. 8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp4 4MB
  75. 7. Clustering/2. Use cases.mp4 4MB
  76. 7. Clustering/9. Implementation of k-means clustering in python.mp4 4MB
  77. 1. Introduction to Machine Learning/3. Machine learning Methods.mp4 4MB
  78. 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp4 3MB
  79. 9. Conclusion/1. Conclusion.mp4 3MB
  80. 1. Introduction to Machine Learning/7.14 u.data 2MB
  81. 1. Introduction to Machine Learning/7.12 Recommender Systems with Python.ipynb 122KB
  82. 1. Introduction to Machine Learning/7.4 K-means algorithm numpy&pandas clustering.ipynb 102KB
  83. 1. Introduction to Machine Learning/7.10 Movie_Id_Titles.original 50KB
  84. 1. Introduction to Machine Learning/7.5 KNN_Binary_Classification.ipynb 25KB
  85. 1. Introduction to Machine Learning/7.6 linear_regression_houseprice.ipynb 16KB
  86. 1. Introduction to Machine Learning/7.2 Decision_tree.ipynb 14KB
  87. 1. Introduction to Machine Learning/7.15 user data.csv 11KB
  88. 1. Introduction to Machine Learning/7.11 MultipleLinearRegression.ipynb 9KB
  89. 8. Recommender System/6. Sorting by title and rating.srt 6KB
  90. 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.srt 6KB
  91. 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.srt 4KB
  92. 1. Introduction to Machine Learning/7.8 mall customers data.csv 4KB
  93. 1. Introduction to Machine Learning/7.9 mallCustomerData.txt 4KB
  94. 7. Clustering/6. Implementation in python.srt 4KB
  95. 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.srt 4KB
  96. 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.srt 3KB
  97. 7. Clustering/10. Importing the dataset.srt 3KB
  98. 8. Recommender System/4. Implementation in python Importing libraries & datasets.srt 3KB
  99. 7. Clustering/11. Visualizing the dataset.srt 3KB
  100. 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.srt 3KB
  101. 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.srt 3KB
  102. 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.srt 3KB
  103. 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.srt 3KB
  104. 7. Clustering/14. 3D Visualization of the predicted values.srt 3KB
  105. 1. Introduction to Machine Learning/7.7 logistic_regression_Binary_Classification.ipynb 3KB
  106. 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.srt 3KB
  107. 8. Recommender System/17. Repeating the process for another movie.srt 3KB
  108. 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.srt 3KB
  109. 1. Introduction to Machine Learning/7.1 50_Startups.csv 2KB
  110. 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.srt 2KB
  111. 8. Recommender System/10. Data pre-processing.srt 2KB
  112. 2. Simple Linear Regression/5. Implementation in python Distribution of the data.srt 2KB
  113. 7. Clustering/15. Number of predicted clusters.srt 2KB
  114. 1. Introduction to Machine Learning/1. What is Machine Learning.srt 2KB
  115. 8. Recommender System/13. Correlation between the most-rated movies.srt 2KB
  116. 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.srt 2KB
  117. 1. Introduction to Machine Learning/2. Applications of Machine Learning.srt 2KB
  118. 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.srt 2KB
  119. 2. Simple Linear Regression/1. Introduction to regression.srt 2KB
  120. 2. Simple Linear Regression/2. How Does Linear Regression Work.srt 2KB
  121. 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.srt 2KB
  122. 7. Clustering/4. Elbow method.srt 2KB
  123. 7. Clustering/8. Density-based clustering.srt 2KB
  124. 7. Clustering/12. Defining the classifier.srt 2KB
  125. 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.srt 2KB
  126. 7. Clustering/13. 3D Visualization of the clusters.srt 2KB
  127. 8. Recommender System/1. Introduction.srt 2KB
  128. 7. Clustering/3. K-Means Clustering Algorithm.srt 2KB
  129. 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.srt 2KB
  130. 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.srt 1KB
  131. 8. Recommender System/12. Grabbing the ratings for two movies.srt 1KB
  132. 8. Recommender System/14. Sorting the data by correlation.srt 1KB
  133. 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.srt 1KB
  134. 3. Multiple Linear Regression/1. Understanding Multiple linear regression.srt 1KB
  135. 5. Classification Algorithms Decision Tree/2. What is Entropy.srt 1KB
  136. 6. Classification Algorithms Logistic regression/1. Introduction.srt 1KB
  137. 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.srt 1KB
  138. 8. Recommender System/9. Jointplot of the ratings and number of ratings.srt 1KB
  139. 5. Classification Algorithms Decision Tree/3. Exploring the dataset.srt 1KB
  140. 5. Classification Algorithms Decision Tree/4. Decision tree structure.srt 1KB
  141. 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.srt 1KB
  142. 1. Introduction to Machine Learning/4. What is Supervised learning.srt 1KB
  143. 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.srt 1KB
  144. 7. Clustering/7. Hierarchical clustering.srt 1KB
  145. 8. Recommender System/8. Frequency distribution.srt 1KB
  146. 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.srt 1KB
  147. 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.srt 1KB
  148. 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.srt 1KB
  149. 8. Recommender System/16. Sorting values.srt 1KB
  150. 7. Clustering/5. Steps of the Elbow method.srt 1KB
  151. 1. Introduction to Machine Learning/5. What is Unsupervised learning.srt 1KB
  152. 7. Clustering/2. Use cases.srt 1KB
  153. 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.srt 1020B
  154. 6. Classification Algorithms Logistic regression/2. Implementation steps.srt 954B
  155. 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.srt 921B
  156. 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.srt 879B
  157. 8. Recommender System/11. Sorting the most-rated movies.srt 879B
  158. 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.srt 869B
  159. 7. Clustering/9. Implementation of k-means clustering in python.srt 836B
  160. 7. Clustering/1. Introduction to clustering.srt 832B
  161. 2. Simple Linear Regression/3. Line representation.srt 828B
  162. 8. Recommender System/7. Histogram showing number of ratings.srt 779B
  163. 8. Recommender System/3. Content-based Recommender System.srt 765B
  164. 8. Recommender System/15. Filtering out movies.srt 726B
  165. 8. Recommender System/2. Collaborative Filtering in Recommender Systems.srt 674B
  166. 1. Introduction to Machine Learning/7.13 salaries.csv 657B
  167. 8. Recommender System/5. Merging datasets into one dataframe.srt 622B
  168. 1. Introduction to Machine Learning/3. Machine learning Methods.srt 437B
  169. 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.srt 434B
  170. 9. Conclusion/1. Conclusion.srt 414B
  171. 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.srt 380B
  172. 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.srt 348B
  173. 8. Recommender System/18. Quiz Time.html 188B
  174. 1. Introduction to Machine Learning/7. Course Materials.html 148B
  175. 0. Websites you may like/[FreeCourseSite.com].url 127B
  176. 0. Websites you may like/[CourseClub.Me].url 122B
  177. 1. Introduction to Machine Learning/7.3 homeprices.csv 77B
  178. 0. Websites you may like/[GigaCourse.Com].url 49B