[Tutorialsplanet.NET] Udemy - Machine Learning in Python with 5 Machine Learning Projects

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Files
  1. 1. Python Fundamentals/1. Why should you learn Python.mp4 65.68MB
  2. 1. Python Fundamentals/1. Why should you learn Python.srt 3.33KB
  3. 1. Python Fundamentals/10. Identity and Membership Operators.mp4 39.22MB
  4. 1. Python Fundamentals/10. Identity and Membership Operators.srt 2.84KB
  5. 1. Python Fundamentals/11. Quiz on Operators.html 147B
  6. 1. Python Fundamentals/12. Quiz Solution.mp4 34.21MB
  7. 1. Python Fundamentals/12. Quiz Solution.srt 4.01KB
  8. 1. Python Fundamentals/13. String Formatting.mp4 51.35MB
  9. 1. Python Fundamentals/13. String Formatting.srt 4.80KB
  10. 1. Python Fundamentals/14. String Methods.mp4 43.29MB
  11. 1. Python Fundamentals/14. String Methods.srt 5.93KB
  12. 1. Python Fundamentals/15. User Input.mp4 41.04MB
  13. 1. Python Fundamentals/15. User Input.srt 2.88KB
  14. 1. Python Fundamentals/16. Quiz on Strings.html 147B
  15. 1. Python Fundamentals/17. Quiz Solution.mp4 53.11MB
  16. 1. Python Fundamentals/17. Quiz Solution.srt 4.35KB
  17. 1. Python Fundamentals/18. If, elif, and else.mp4 65.90MB
  18. 1. Python Fundamentals/18. If, elif, and else.srt 3.75KB
  19. 1. Python Fundamentals/19. For and While.mp4 53.07MB
  20. 1. Python Fundamentals/19. For and While.srt 5.26KB
  21. 1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp4 33.48MB
  22. 1. Python Fundamentals/2. Installing Python and Jupyter Notebook.srt 2.44KB
  23. 1. Python Fundamentals/20. Break and Continue.mp4 40.72MB
  24. 1. Python Fundamentals/20. Break and Continue.srt 2.86KB
  25. 1. Python Fundamentals/21. Quiz on Loops and Conditionals.html 147B
  26. 1. Python Fundamentals/22. Quiz Solution.mp4 49.04MB
  27. 1. Python Fundamentals/22. Quiz Solution.srt 4.28KB
  28. 1. Python Fundamentals/3. Naming Convention for Variables.mp4 102.24MB
  29. 1. Python Fundamentals/3. Naming Convention for Variables.srt 6.05KB
  30. 1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4 119.86MB
  31. 1. Python Fundamentals/4. Built in Data Types and Type Casting.srt 6.54KB
  32. 1. Python Fundamentals/5. Scope of Variables.mp4 77.16MB
  33. 1. Python Fundamentals/5. Scope of Variables.srt 4.10KB
  34. 1. Python Fundamentals/6. Quiz on Variables and Data Types.html 147B
  35. 1. Python Fundamentals/7. Quiz Solution.mp4 46.52MB
  36. 1. Python Fundamentals/7. Quiz Solution.srt 5.29KB
  37. 1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4 78.04MB
  38. 1. Python Fundamentals/8. Arithmetic and Assignment Operators.srt 7.98KB
  39. 1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4 62.41MB
  40. 1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.srt 7.46KB
  41. 1. Python Fundamentals/[Tutorialsplanet.NET].url 128B
  42. 10. Logistic Regression/1. Introduction to Logistic Regression.mp4 106.40MB
  43. 10. Logistic Regression/1. Introduction to Logistic Regression.srt 6.83KB
  44. 10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4 59.89MB
  45. 10. Logistic Regression/10. Industry Relevance of Logistic Regression.srt 3.34KB
  46. 10. Logistic Regression/11. Quiz on Modelling with Logistic Regression.html 147B
  47. 10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4 87.01MB
  48. 10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.srt 9.17KB
  49. 10. Logistic Regression/3. Feature Selection using RFECV.mp4 42.15MB
  50. 10. Logistic Regression/3. Feature Selection using RFECV.srt 3.00KB
  51. 10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4 58.75MB
  52. 10. Logistic Regression/4. Hyperparameter tuning using Grid search.srt 5.00KB
  53. 10. Logistic Regression/5. Applying Cross Validation.mp4 56.73MB
  54. 10. Logistic Regression/5. Applying Cross Validation.srt 3.81KB
  55. 10. Logistic Regression/6. How to analyze performance of a classification model.mp4 146.18MB
  56. 10. Logistic Regression/6. How to analyze performance of a classification model.srt 8.73KB
  57. 10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4 55.54MB
  58. 10. Logistic Regression/7. Using accuracy score to analyze the performance of model.srt 4.65KB
  59. 10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4 147.63MB
  60. 10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.srt 9.47KB
  61. 10. Logistic Regression/9. Real time prediction using logistic regression.mp4 74.65MB
  62. 10. Logistic Regression/9. Real time prediction using logistic regression.srt 7.01KB
  63. 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4 108.17MB
  64. 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.srt 5.76KB
  65. 11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4 70.38MB
  66. 11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.srt 3.77KB
  67. 11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4 67.43MB
  68. 11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.srt 7.44KB
  69. 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4 104.32MB
  70. 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.srt 5.39KB
  71. 11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp4 33.23MB
  72. 11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.srt 2.01KB
  73. 11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4 174.72MB
  74. 11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.srt 10.41KB
  75. 11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4 61.96MB
  76. 11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.srt 3.41KB
  77. 11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4 69.77MB
  78. 11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.srt 3.98KB
  79. 11. Introduction to KNN, SVM, Naive Bayes/9. Quiz on Other classification models.html 147B
  80. 12. Tree Based Models/1. Intuition for decision trees.mp4 81.99MB
  81. 12. Tree Based Models/1. Intuition for decision trees.srt 4.56KB
  82. 12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4 218.66MB
  83. 12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.srt 13.24KB
  84. 12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4 53.37MB
  85. 12. Tree Based Models/3. Advantages and Issues with Decision trees.srt 2.97KB
  86. 12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp4 35.80MB
  87. 12. Tree Based Models/4. Implementing Decision tree using Sklearn.srt 3.62KB
  88. 12. Tree Based Models/5. Understanding the concept of Bagging.mp4 65.99MB
  89. 12. Tree Based Models/5. Understanding the concept of Bagging.srt 3.62KB
  90. 12. Tree Based Models/6. Introduction to Random forest.mp4 68.09MB
  91. 12. Tree Based Models/6. Introduction to Random forest.srt 3.97KB
  92. 12. Tree Based Models/7. Understanding the parameters of Random forest.mp4 53.66MB
  93. 12. Tree Based Models/7. Understanding the parameters of Random forest.srt 4.27KB
  94. 12. Tree Based Models/8. Implementing random forest using Sklearn.mp4 47.88MB
  95. 12. Tree Based Models/8. Implementing random forest using Sklearn.srt 4.35KB
  96. 12. Tree Based Models/9. Quiz on Tree based models.html 147B
  97. 13. Boosting Models/1. Understading the concept of boosting.mp4 57.14MB
  98. 13. Boosting Models/1. Understading the concept of boosting.srt 3.04KB
  99. 13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4 153.30MB
  100. 13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.srt 8.64KB
  101. 13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4 90.82MB
  102. 13. Boosting Models/3. Implementing AdaBoost using sklearn.srt 9.37KB
  103. 13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4 66.93MB
  104. 13. Boosting Models/4. Implementing Gradient Boosting using sklearn.srt 4.77KB
  105. 13. Boosting Models/5. Getting High level intuition for XGBoost.mp4 41.07MB
  106. 13. Boosting Models/5. Getting High level intuition for XGBoost.srt 2.04KB
  107. 13. Boosting Models/6. Implementing XGBoost using sklearn.mp4 65.14MB
  108. 13. Boosting Models/6. Implementing XGBoost using sklearn.srt 4.69KB
  109. 13. Boosting Models/7. Introudction to Ensembling techniques.mp4 134.02MB
  110. 13. Boosting Models/7. Introudction to Ensembling techniques.srt 7.81KB
  111. 13. Boosting Models/8. Quiz on Boosting Models.html 147B
  112. 13. Boosting Models/[Tutorialsplanet.NET].url 128B
  113. 14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4 53.62MB
  114. 14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.srt 3.19KB
  115. 14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4 57.45MB
  116. 14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.srt 4.04KB
  117. 14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4 64.02MB
  118. 14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.srt 4.56KB
  119. 14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4 55.76MB
  120. 14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.srt 3.59KB
  121. 14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4 54.88MB
  122. 14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.srt 3.71KB
  123. 14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4 43.54MB
  124. 14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.srt 3.51KB
  125. 14. Imbalanced Machine Learning/15. Comparing the Results.mp4 41.50MB
  126. 14. Imbalanced Machine Learning/15. Comparing the Results.srt 2.14KB
  127. 14. Imbalanced Machine Learning/16. Quiz on Handling Imbalanced Datasets.html 147B
  128. 14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4 70.55MB
  129. 14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.srt 4.26KB
  130. 14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4 56.98MB
  131. 14. Imbalanced Machine Learning/3. Solving a Real World Problem.srt 4.16KB
  132. 14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4 57.93MB
  133. 14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.srt 4.78KB
  134. 14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4 71.14MB
  135. 14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.srt 5.08KB
  136. 14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4 42.65MB
  137. 14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.srt 3.14KB
  138. 14. Imbalanced Machine Learning/7. Quiz on Introduction to Imbalanced Machine Learning.html 147B
  139. 14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4 54.41MB
  140. 14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.srt 4.22KB
  141. 14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4 57.54MB
  142. 14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.srt 4.00KB
  143. 15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4 57.84MB
  144. 15. Introduction to Clustering Analysis/1. Introduction to Clustering.srt 3.14KB
  145. 15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4 50.01MB
  146. 15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.srt 4.02KB
  147. 15. Introduction to Clustering Analysis/11. Quiz on K Means Clustering.html 147B
  148. 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4 88.49MB
  149. 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.srt 4.75KB
  150. 15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4 41.78MB
  151. 15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.srt 3.93KB
  152. 15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4 52.35MB
  153. 15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.srt 3.62KB
  154. 15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4 52.38MB
  155. 15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.srt 3.62KB
  156. 15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4 47.87MB
  157. 15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.srt 3.58KB
  158. 15. Introduction to Clustering Analysis/17. Quiz on Advanced Clustering Techniques.html 147B
  159. 15. Introduction to Clustering Analysis/2. Types of Clustering.mp4 65.18MB
  160. 15. Introduction to Clustering Analysis/2. Types of Clustering.srt 3.87KB
  161. 15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4 55.95MB
  162. 15. Introduction to Clustering Analysis/3. Applications of Clustering.srt 3.29KB
  163. 15. Introduction to Clustering Analysis/4. Quiz on Introduction to Clustering.html 147B
  164. 15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4 67.06MB
  165. 15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.srt 3.64KB
  166. 15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4 49.29MB
  167. 15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.srt 3.77KB
  168. 15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4 71.00MB
  169. 15. Introduction to Clustering Analysis/7. Solving a Real World Problem.srt 4.94KB
  170. 15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4 71.57MB
  171. 15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.srt 6.23KB
  172. 15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4 96.34MB
  173. 15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.srt 6.93KB
  174. 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4 79.22MB
  175. 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.srt 4.32KB
  176. 16. Dimensionality Reduction/10. Quiz on Variance Filtering.html 147B
  177. 16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4 56.62MB
  178. 16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.srt 3.13KB
  179. 16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4 50.92MB
  180. 16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.srt 4.25KB
  181. 16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4 52.48MB
  182. 16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.srt 2.97KB
  183. 16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4 43.20MB
  184. 16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.srt 4.48KB
  185. 16. Dimensionality Reduction/15. Quiz on Feature Selection.html 147B
  186. 16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4 73.79MB
  187. 16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.srt 4.12KB
  188. 16. Dimensionality Reduction/17. Implementing PCA.mp4 55.52MB
  189. 16. Dimensionality Reduction/17. Implementing PCA.srt 4.32KB
  190. 16. Dimensionality Reduction/18. Introduction to t-SNE.mp4 81.27MB
  191. 16. Dimensionality Reduction/18. Introduction to t-SNE.srt 4.46KB
  192. 16. Dimensionality Reduction/19. Implementing t-SNE.mp4 36.11MB
  193. 16. Dimensionality Reduction/19. Implementing t-SNE.srt 2.34KB
  194. 16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4 57.16MB
  195. 16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.srt 3.33KB
  196. 16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4 48.90MB
  197. 16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.srt 2.69KB
  198. 16. Dimensionality Reduction/21. Implementing LDA.mp4 36.74MB
  199. 16. Dimensionality Reduction/21. Implementing LDA.srt 2.69KB
  200. 16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4 64.79MB
  201. 16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.srt 3.35KB
  202. 16. Dimensionality Reduction/23. Quiz on Machine Learning.html 147B
  203. 16. Dimensionality Reduction/3. Solving a Real World Problem.mp4 98.82MB
  204. 16. Dimensionality Reduction/3. Solving a Real World Problem.srt 8.41KB
  205. 16. Dimensionality Reduction/4. Quiz on Introduction.html 147B
  206. 16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4 71.40MB
  207. 16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.srt 5.39KB
  208. 16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4 48.87MB
  209. 16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.srt 3.96KB
  210. 16. Dimensionality Reduction/7. Quiz on Correlation Filtering.html 147B
  211. 16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4 48.66MB
  212. 16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.srt 2.34KB
  213. 16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4 47.84MB
  214. 16. Dimensionality Reduction/9. Implementing VIF using statsmodel.srt 3.58KB
  215. 17. Recommendation Engines/1. Introduction to Recommender systems.mp4 40.53MB
  216. 17. Recommendation Engines/1. Introduction to Recommender systems.srt 2.18KB
  217. 17. Recommendation Engines/10. Quiz on Content Based Filtering.html 147B
  218. 17. Recommendation Engines/11. Quiz Solution.mp4 48.50MB
  219. 17. Recommendation Engines/11. Quiz Solution.srt 4.38KB
  220. 17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4 80.86MB
  221. 17. Recommendation Engines/12. Introduction to Collaborative Filtering.srt 4.76KB
  222. 17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4 72.39MB
  223. 17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.srt 5.69KB
  224. 17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4 62.15MB
  225. 17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.srt 4.61KB
  226. 17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4 63.59MB
  227. 17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.srt 5.58KB
  228. 17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4 63.55MB
  229. 17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.srt 4.43KB
  230. 17. Recommendation Engines/17. Quiz on Collaborative Based Filtering.html 147B
  231. 17. Recommendation Engines/18. Quiz Solution.mp4 55.62MB
  232. 17. Recommendation Engines/18. Quiz Solution.srt 4.56KB
  233. 17. Recommendation Engines/19. Introduction to SVD.mp4 112.02MB
  234. 17. Recommendation Engines/19. Introduction to SVD.srt 5.95KB
  235. 17. Recommendation Engines/2. What are it's Use Cases.mp4 45.05MB
  236. 17. Recommendation Engines/2. What are it's Use Cases.srt 2.42KB
  237. 17. Recommendation Engines/20. Implementing SVD using Surprise.mp4 40.63MB
  238. 17. Recommendation Engines/20. Implementing SVD using Surprise.srt 3.81KB
  239. 17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4 46.01MB
  240. 17. Recommendation Engines/21. Interpreting Results Obtained from SVD.srt 5.40KB
  241. 17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4 61.99MB
  242. 17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.srt 3.70KB
  243. 17. Recommendation Engines/23. Quiz on Singular Value Decomposition.html 147B
  244. 17. Recommendation Engines/24. Quiz Solution.mp4 47.94MB
  245. 17. Recommendation Engines/24. Quiz Solution.srt 3.59KB
  246. 17. Recommendation Engines/25. Case Study for Netflix.mp4 56.38MB
  247. 17. Recommendation Engines/25. Case Study for Netflix.srt 2.98KB
  248. 17. Recommendation Engines/26. Case Study for Youtube.mp4 58.14MB
  249. 17. Recommendation Engines/26. Case Study for Youtube.srt 3.00KB
  250. 17. Recommendation Engines/3. Types of Recommender Systems.mp4 56.54MB
  251. 17. Recommendation Engines/3. Types of Recommender Systems.srt 3.41KB
  252. 17. Recommendation Engines/4. Evaluating Recommender Systems.mp4 53.15MB
  253. 17. Recommendation Engines/4. Evaluating Recommender Systems.srt 3.00KB
  254. 17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4 59.00MB
  255. 17. Recommendation Engines/5. Introduction to Content Based Filtering.srt 3.65KB
  256. 17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4 76.67MB
  257. 17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.srt 6.63KB
  258. 17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4 58.73MB
  259. 17. Recommendation Engines/7. Filtering Movies Based on Genres.srt 5.82KB
  260. 17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4 63.39MB
  261. 17. Recommendation Engines/8. Introduction to Transactional Encoder.srt 3.21KB
  262. 17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4 56.31MB
  263. 17. Recommendation Engines/9. Recommending Similar Movies to Watch.srt 3.99KB
  264. 18. Time Series Forecasting/1. What is a Time Series Data.mp4 34.91MB
  265. 18. Time Series Forecasting/1. What is a Time Series Data.srt 1.95KB
  266. 18. Time Series Forecasting/10. Time Series Decomposition.mp4 89.93MB
  267. 18. Time Series Forecasting/10. Time Series Decomposition.srt 7.63KB
  268. 18. Time Series Forecasting/11. Splitting Time Series Data.mp4 63.50MB
  269. 18. Time Series Forecasting/11. Splitting Time Series Data.srt 4.06KB
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