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GetFreeCourses.Co-Udemy-Machine Learning & Data Science A-Z Hands-on Python 2021

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文件列表

  1. 6. Supervised Learning - Regression/8. Random Forest Model Development.mp4 246MB
  2. 5. Supervised Learning - Classification/1. Supervised Learning Models - Introduction and Understanding the Data.mp4 234MB
  3. 5. Supervised Learning - Classification/4. k-NN Training-Set and Test-Set Creation.mp4 228MB
  4. 3. Data Preprocessing/6. Missing Values2.mp4 219MB
  5. 6. Supervised Learning - Regression/1. Simple and Multiple Linear Regression Concepts.mp4 212MB
  6. 3. Data Preprocessing/3. Statistics2.mp4 207MB
  7. 6. Supervised Learning - Regression/6. Polynomial Linear Regression Model Development.mp4 207MB
  8. 2. Machine Learning Useful Packages (Libraries)/13. Visualization with Matplotlib2.mp4 205MB
  9. 2. Machine Learning Useful Packages (Libraries)/11. Pandas4.mp4 203MB
  10. 2. Machine Learning Useful Packages (Libraries)/14. Visualization with Matplotlib3.mp4 189MB
  11. 3. Data Preprocessing/12. Normalization.mp4 187MB
  12. 5. Supervised Learning - Classification/13. Model Evaluation - Calculating with Python.mp4 174MB
  13. 6. Supervised Learning - Regression/4. Evaluation Metrics - Implementation.mp4 160MB
  14. 3. Data Preprocessing/1. Reading and Modifying a Dataset.mp4 155MB
  15. 2. Machine Learning Useful Packages (Libraries)/6. NumPy5.mp4 153MB
  16. 7. Unsupervised Learning - Clustering Techniques/10. Hierarchical Clustering Model Development.mp4 146MB
  17. 2. Machine Learning Useful Packages (Libraries)/15. Visualization with Matplotlib4.mp4 143MB
  18. 5. Supervised Learning - Classification/3. k-NN Model Development.mp4 141MB
  19. 2. Machine Learning Useful Packages (Libraries)/7. NumPy6.mp4 134MB
  20. 8. Hyper Parameter Optimization (Model Tuning)/4. k-NN - Model Tuning.mp4 134MB
  21. 3. Data Preprocessing/8. Outlier Detection2.mp4 131MB
  22. 3. Data Preprocessing/5. Missing Values1.mp4 130MB
  23. 2. Machine Learning Useful Packages (Libraries)/16. Visualization with Matplotlib5.mp4 129MB
  24. 8. Hyper Parameter Optimization (Model Tuning)/2. Support Vector Regression - Model Tuning.mp4 126MB
  25. 6. Supervised Learning - Regression/10. Support Vector Regression Model Development.mp4 121MB
  26. 2. Machine Learning Useful Packages (Libraries)/10. Pandas3.mp4 118MB
  27. 2. Machine Learning Useful Packages (Libraries)/9. Pandas2.mp4 117MB
  28. 5. Supervised Learning - Classification/11. Logistic Regression Model Development.mp4 112MB
  29. 3. Data Preprocessing/4. Statistics3 - Covariance.mp4 107MB
  30. 7. Unsupervised Learning - Clustering Techniques/5. K-means Model Development2.mp4 104MB
  31. 7. Unsupervised Learning - Clustering Techniques/6. K-means - Model Evaluation.mp4 102MB
  32. 2. Machine Learning Useful Packages (Libraries)/12. Visualization with Matplotlib1.mp4 99MB
  33. 2. Machine Learning Useful Packages (Libraries)/8. Pandas1.mp4 96MB
  34. 7. Unsupervised Learning - Clustering Techniques/8. DBSCAN Model Development.mp4 87MB
  35. 2. Machine Learning Useful Packages (Libraries)/4. NumPy3.mp4 85MB
  36. 5. Supervised Learning - Classification/12. Model Evaluation Concepts.mp4 83MB
  37. 6. Supervised Learning - Regression/2. Multiple Linear Regression - Model Development.mp4 76MB
  38. 3. Data Preprocessing/7. Outlier Detection1.mp4 73MB
  39. 8. Hyper Parameter Optimization (Model Tuning)/5. Overfitting and Underfitting.mp4 72MB
  40. 1. Introduction/6. Installation of Required Libraries.mp4 71MB
  41. 5. Supervised Learning - Classification/6. Decision Tree Model Development.mp4 67MB
  42. 3. Data Preprocessing/10. Concatenation.mp4 66MB
  43. 5. Supervised Learning - Classification/8. Naive Bayes Concepts.mp4 59MB
  44. 5. Supervised Learning - Classification/9. Naive Bayes Model Development.mp4 59MB
  45. 3. Data Preprocessing/11. Dummy Variable.mp4 58MB
  46. 2. Machine Learning Useful Packages (Libraries)/3. NumPy2.mp4 57MB
  47. 2. Machine Learning Useful Packages (Libraries)/5. NumPy4.mp4 57MB
  48. 5. Supervised Learning - Classification/7. Decision Tree - Cross Validation.mp4 55MB
  49. 6. Supervised Learning - Regression/3. Evaluation Metrics - Concepts.mp4 49MB
  50. 5. Supervised Learning - Classification/2. k-NN Concepts.mp4 48MB
  51. 1. Introduction/7. Spyder Interface.mp4 46MB
  52. 4. Machine Learning Introduction/1. Learning Types.mp4 45MB
  53. 7. Unsupervised Learning - Clustering Techniques/2. K-means Concepts1.mp4 45MB
  54. 7. Unsupervised Learning - Clustering Techniques/1. Introduction.mp4 38MB
  55. 2. Machine Learning Useful Packages (Libraries)/2. NumPy1.mp4 37MB
  56. 7. Unsupervised Learning - Clustering Techniques/4. K-means Model Development1.mp4 36MB
  57. 3. Data Preprocessing/2. Statistics1.mp4 34MB
  58. 3. Data Preprocessing/9. Outlier Detection3.mp4 31MB
  59. 6. Supervised Learning - Regression/7. Random Forest Concepts.mp4 30MB
  60. 6. Supervised Learning - Regression/9. Support Vector Regression Concepts.mp4 27MB
  61. 7. Unsupervised Learning - Clustering Techniques/7. DBSCAN Concepts.mp4 27MB
  62. 6. Supervised Learning - Regression/5. Polynomial Linear Regression Concepts.mp4 26MB
  63. 1. Introduction/2. What is Machine Learning Some Basic Terms.mp4 26MB
  64. 5. Supervised Learning - Classification/5. Decision Tree Concepts.mp4 26MB
  65. 7. Unsupervised Learning - Clustering Techniques/9. Hierarchical Clustering Concepts.mp4 24MB
  66. 1. Introduction/5. IDE Installation.mp4 22MB
  67. 7. Unsupervised Learning - Clustering Techniques/3. K-means Concepts2.mp4 21MB
  68. 1. Introduction/1. Course Content.mp4 17MB
  69. 8. Hyper Parameter Optimization (Model Tuning)/1. Introduction.mp4 17MB
  70. 8. Hyper Parameter Optimization (Model Tuning)/3. K-Means - Model Tuning.mp4 15MB
  71. 5. Supervised Learning - Classification/10. Logistic Regression Concepts.mp4 11MB
  72. 1. Introduction/4. Python IDE.mp4 8MB
  73. 5. Supervised Learning - Classification/1. Supervised Learning Models - Introduction and Understanding the Data.srt 33KB
  74. 6. Supervised Learning - Regression/1. Simple and Multiple Linear Regression Concepts.srt 31KB
  75. 5. Supervised Learning - Classification/4. k-NN Training-Set and Test-Set Creation.srt 28KB
  76. 2. Machine Learning Useful Packages (Libraries)/11. Pandas4.srt 27KB
  77. 2. Machine Learning Useful Packages (Libraries)/13. Visualization with Matplotlib2.srt 26KB
  78. 6. Supervised Learning - Regression/8. Random Forest Model Development.srt 25KB
  79. 3. Data Preprocessing/6. Missing Values2.srt 22KB
  80. 3. Data Preprocessing/1. Reading and Modifying a Dataset.srt 22KB
  81. 3. Data Preprocessing/12. Normalization.srt 22KB
  82. 3. Data Preprocessing/3. Statistics2.srt 22KB
  83. 6. Supervised Learning - Regression/6. Polynomial Linear Regression Model Development.srt 21KB
  84. 5. Supervised Learning - Classification/13. Model Evaluation - Calculating with Python.srt 20KB
  85. 2. Machine Learning Useful Packages (Libraries)/14. Visualization with Matplotlib3.srt 20KB
  86. 5. Supervised Learning - Classification/12. Model Evaluation Concepts.srt 19KB
  87. 2. Machine Learning Useful Packages (Libraries)/6. NumPy5.srt 19KB
  88. 2. Machine Learning Useful Packages (Libraries)/7. NumPy6.srt 18KB
  89. 6. Supervised Learning - Regression/4. Evaluation Metrics - Implementation.srt 18KB
  90. 7. Unsupervised Learning - Clustering Techniques/10. Hierarchical Clustering Model Development.srt 18KB
  91. 2. Machine Learning Useful Packages (Libraries)/9. Pandas2.srt 17KB
  92. 2. Machine Learning Useful Packages (Libraries)/8. Pandas1.srt 17KB
  93. 2. Machine Learning Useful Packages (Libraries)/15. Visualization with Matplotlib4.srt 17KB
  94. 2. Machine Learning Useful Packages (Libraries)/10. Pandas3.srt 17KB
  95. 5. Supervised Learning - Classification/3. k-NN Model Development.srt 17KB
  96. 5. Supervised Learning - Classification/8. Naive Bayes Concepts.srt 16KB
  97. 2. Machine Learning Useful Packages (Libraries)/12. Visualization with Matplotlib1.srt 16KB
  98. 3. Data Preprocessing/4. Statistics3 - Covariance.srt 16KB
  99. 3. Data Preprocessing/8. Outlier Detection2.srt 15KB
  100. 2. Machine Learning Useful Packages (Libraries)/16. Visualization with Matplotlib5.srt 14KB
  101. 3. Data Preprocessing/5. Missing Values1.srt 14KB
  102. 7. Unsupervised Learning - Clustering Techniques/5. K-means Model Development2.srt 14KB
  103. 2. Machine Learning Useful Packages (Libraries)/4. NumPy3.srt 14KB
  104. 8. Hyper Parameter Optimization (Model Tuning)/2. Support Vector Regression - Model Tuning.srt 14KB
  105. 3. Data Preprocessing/7. Outlier Detection1.srt 13KB
  106. 8. Hyper Parameter Optimization (Model Tuning)/4. k-NN - Model Tuning.srt 13KB
  107. 6. Supervised Learning - Regression/3. Evaluation Metrics - Concepts.srt 13KB
  108. 5. Supervised Learning - Classification/11. Logistic Regression Model Development.srt 12KB
  109. 7. Unsupervised Learning - Clustering Techniques/6. K-means - Model Evaluation.srt 12KB
  110. 5. Supervised Learning - Classification/2. k-NN Concepts.srt 12KB
  111. 6. Supervised Learning - Regression/10. Support Vector Regression Model Development.srt 11KB
  112. 7. Unsupervised Learning - Clustering Techniques/2. K-means Concepts1.srt 11KB
  113. 8. Hyper Parameter Optimization (Model Tuning)/5. Overfitting and Underfitting.srt 11KB
  114. 7. Unsupervised Learning - Clustering Techniques/8. DBSCAN Model Development.srt 10KB
  115. 3. Data Preprocessing/2. Statistics1.srt 10KB
  116. 2. Machine Learning Useful Packages (Libraries)/3. NumPy2.srt 10KB
  117. 5. Supervised Learning - Classification/7. Decision Tree - Cross Validation.srt 10KB
  118. 1. Introduction/7. Spyder Interface.srt 9KB
  119. 4. Machine Learning Introduction/1. Learning Types.srt 9KB
  120. 6. Supervised Learning - Regression/2. Multiple Linear Regression - Model Development.srt 9KB
  121. 1. Introduction/6. Installation of Required Libraries.srt 9KB
  122. 7. Unsupervised Learning - Clustering Techniques/1. Introduction.srt 8KB
  123. 2. Machine Learning Useful Packages (Libraries)/2. NumPy1.srt 8KB
  124. 3. Data Preprocessing/10. Concatenation.srt 8KB
  125. 3. Data Preprocessing/11. Dummy Variable.srt 8KB
  126. 2. Machine Learning Useful Packages (Libraries)/5. NumPy4.srt 8KB
  127. 5. Supervised Learning - Classification/5. Decision Tree Concepts.srt 8KB
  128. 6. Supervised Learning - Regression/9. Support Vector Regression Concepts.srt 8KB
  129. 2. Machine Learning Useful Packages (Libraries)/1.1 Python Source Codes.zip 7KB
  130. 6. Supervised Learning - Regression/7. Random Forest Concepts.srt 7KB
  131. 7. Unsupervised Learning - Clustering Techniques/3. K-means Concepts2.srt 7KB
  132. 1. Introduction/2. What is Machine Learning Some Basic Terms.srt 7KB
  133. 5. Supervised Learning - Classification/6. Decision Tree Model Development.srt 7KB
  134. 5. Supervised Learning - Classification/9. Naive Bayes Model Development.srt 7KB
  135. 6. Supervised Learning - Regression/5. Polynomial Linear Regression Concepts.srt 7KB
  136. 7. Unsupervised Learning - Clustering Techniques/9. Hierarchical Clustering Concepts.srt 7KB
  137. 1. Introduction/1. Course Content.srt 6KB
  138. 7. Unsupervised Learning - Clustering Techniques/7. DBSCAN Concepts.srt 6KB
  139. 7. Unsupervised Learning - Clustering Techniques/4. K-means Model Development1.srt 5KB
  140. 8. Hyper Parameter Optimization (Model Tuning)/1. Introduction.srt 5KB
  141. 3. Data Preprocessing/9. Outlier Detection3.srt 4KB
  142. 5. Supervised Learning - Classification/10. Logistic Regression Concepts.srt 3KB
  143. 1. Introduction/5. IDE Installation.srt 3KB
  144. 1. Introduction/4. Python IDE.srt 3KB
  145. 8. Hyper Parameter Optimization (Model Tuning)/3. K-Means - Model Tuning.srt 3KB
  146. 1. Introduction/3. Python Installation.html 612B
  147. 2. Machine Learning Useful Packages (Libraries)/11.1 Data_Set.csv 580B
  148. 3. Data Preprocessing/1.1 Data_Set.csv 580B
  149. 2. Machine Learning Useful Packages (Libraries)/1. Python Source Codes.html 368B
  150. 3. Data Preprocessing/10.1 Data_New.csv 201B
  151. 2. Machine Learning Useful Packages (Libraries)/17. Chapter 2 Quiz.html 160B
  152. 3. Data Preprocessing/13. Chapter3 Quiz.html 160B
  153. 4. Machine Learning Introduction/2. Chapter 4 Quiz.html 160B
  154. 5. Supervised Learning - Classification/14. Chapter 5 Quiz.html 160B
  155. 6. Supervised Learning - Regression/11. Chapter 6 Quiz.html 160B
  156. 7. Unsupervised Learning - Clustering Techniques/11. Chapter 7 Quiz.html 160B
  157. 3. Data Preprocessing/GetFreeCourses.Co.url 116B
  158. 5. Supervised Learning - Classification/GetFreeCourses.Co.url 116B
  159. Download Paid Udemy Courses For Free.url 116B
  160. GetFreeCourses.Co.url 116B