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[] Udemy - machinelearning

  • 收录时间:2018-03-08 07:28:28
  • 文件大小:5GB
  • 下载次数:478
  • 最近下载:2021-01-14 09:35:36
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文件列表

  1. 36 Kernel PCA/264 Kernel PCA in R.mp4 57MB
  2. 12 Logistic Regression/089 Logistic Regression in R - Step 5.mp4 52MB
  3. 35 Linear Discriminant Analysis (LDA)/261 LDA in R.mp4 51MB
  4. 17 Decision Tree Classification/116 Decision Tree Classification in R.mp4 51MB
  5. 18 Random Forest Classification/120 Random Forest Classification in R.mp4 49MB
  6. 31 Artificial Neural Networks/217 ANN in Python - Step 2.mp4 48MB
  7. 39 XGBoost/275 XGBoost in R.mp4 47MB
  8. 27 Upper Confidence Bound (UCB)/171 Upper Confidence Bound in R - Step 3.mp4 47MB
  9. 18 Random Forest Classification/119 Random Forest Classification in Python.mp4 47MB
  10. 32 Convolutional Neural Networks/248 CNN in Python - Step 9.mp4 47MB
  11. 07 Support Vector Regression (SVR)/061 SVR in Python.mp4 46MB
  12. 35 Linear Discriminant Analysis (LDA)/260 LDA in Python.mp4 45MB
  13. 08 Decision Tree Regression/066 Decision Tree Regression in R.mp4 44MB
  14. 16 Naive Bayes/106 Bayes Theorem.mp4 44MB
  15. 24 Apriori/154 Apriori in R - Step 3.mp4 44MB
  16. 38 Model Selection/268 k-Fold Cross Validation in R.mp4 44MB
  17. 06 Polynomial Regression/057 Polynomial Regression in R - Step 3.mp4 43MB
  18. 28 Thompson Sampling/176 Thompson Sampling in Python - Step 1.mp4 43MB
  19. 06 Polynomial Regression/052 Polynomial Regression in Python - Step 3.mp4 43MB
  20. 24 Apriori/152 Apriori in R - Step 1.mp4 43MB
  21. 32 Convolutional Neural Networks/236 Step 4 - Full Connection.mp4 43MB
  22. 12 Logistic Regression/083 Logistic Regression in Python - Step 5.mp4 43MB
  23. 15 Kernel SVM/104 Kernel SVM in Python.mp4 42MB
  24. 13 K-Nearest Neighbors (K-NN)/094 K-NN in R.mp4 41MB
  25. 29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10.mp4 41MB
  26. 27 Upper Confidence Bound (UCB)/167 Upper Confidence Bound in Python - Step 3.mp4 41MB
  27. 28 Thompson Sampling/178 Thompson Sampling in R - Step 1.mp4 41MB
  28. 02 -------------------- Part 1_ Data Preprocessing --------------------/013 Categorical Data.mp4 41MB
  29. 15 Kernel SVM/105 Kernel SVM in R.mp4 40MB
  30. 29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1.mp4 40MB
  31. 09 Random Forest Regression/070 Random Forest Regression in R.mp4 40MB
  32. 32 Convolutional Neural Networks/234 Step 2 - Pooling.mp4 40MB
  33. 21 K-Means Clustering/132 K-Means Clustering in Python.mp4 40MB
  34. 05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40MB
  35. 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1.mp4 40MB
  36. 29 -------------------- Part 7_ Natural Language Processing --------------------/189 Natural Language Processing in Python - Step 8.mp4 39MB
  37. 09 Random Forest Regression/069 Random Forest Regression in Python.mp4 39MB
  38. 02 -------------------- Part 1_ Data Preprocessing --------------------/014 Splitting the Dataset into the Training set and Test set.mp4 39MB
  39. 31 Artificial Neural Networks/226 ANN in R - Step 1.mp4 39MB
  40. 38 Model Selection/269 Grid Search in Python - Step 1.mp4 38MB
  41. 24 Apriori/155 Apriori in Python - Step 1.mp4 38MB
  42. 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4.mp4 37MB
  43. 16 Naive Bayes/112 Naive Bayes in R.mp4 37MB
  44. 28 Thompson Sampling/173 Thompson Sampling Intuition.mp4 37MB
  45. 34 Principal Component Analysis (PCA)/258 PCA in R - Step 3.mp4 37MB
  46. 38 Model Selection/271 Grid Search in R.mp4 36MB
  47. 27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2.mp4 35MB
  48. 13 K-Nearest Neighbors (K-NN)/093 K-NN in Python.mp4 35MB
  49. 29 -------------------- Part 7_ Natural Language Processing --------------------/182 Natural Language Processing in Python - Step 1.mp4 35MB
  50. 24 Apriori/150 Apriori Intuition.mp4 35MB
  51. 02 -------------------- Part 1_ Data Preprocessing --------------------/015 Feature Scaling.mp4 35MB
  52. 08 Decision Tree Regression/065 Decision Tree Regression in Python.mp4 34MB
  53. 31 Artificial Neural Networks/229 ANN in R - Step 4 (Last step).mp4 33MB
  54. 36 Kernel PCA/263 Kernel PCA in Python.mp4 33MB
  55. 32 Convolutional Neural Networks/238 Softmax & Cross-Entropy.mp4 33MB
  56. 38 Model Selection/267 k-Fold Cross Validation in Python.mp4 33MB
  57. 05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 33MB
  58. 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 33MB
  59. 14 Support Vector Machine (SVM)/098 SVM in R.mp4 32MB
  60. 02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data.mp4 32MB
  61. 39 XGBoost/274 XGBoost in Python - Step 2.mp4 32MB
  62. 34 Principal Component Analysis (PCA)/253 PCA in Python - Step 1.mp4 32MB
  63. 27 Upper Confidence Bound (UCB)/165 Upper Confidence Bound in Python - Step 1.mp4 32MB
  64. 30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_.mp4 31MB
  65. 14 Support Vector Machine (SVM)/097 SVM in Python.mp4 31MB
  66. 32 Convolutional Neural Networks/232 Step 1 - Convolution Operation.mp4 31MB
  67. 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 4.mp4 31MB
  68. 34 Principal Component Analysis (PCA)/256 PCA in R - Step 1.mp4 31MB
  69. 24 Apriori/153 Apriori in R - Step 2.mp4 30MB
  70. 27 Upper Confidence Bound (UCB)/162 The Multi-Armed Bandit Problem.mp4 30MB
  71. 31 Artificial Neural Networks/207 The Neuron.mp4 30MB
  72. 17 Decision Tree Classification/115 Decision Tree Classification in Python.mp4 30MB
  73. 31 Artificial Neural Networks/220 ANN in Python - Step 5.mp4 30MB
  74. 24 Apriori/156 Apriori in Python - Step 2.mp4 30MB
  75. 38 Model Selection/270 Grid Search in Python - Step 2.mp4 30MB
  76. 32 Convolutional Neural Networks/231 What are convolutional neural networks_.mp4 29MB
  77. 27 Upper Confidence Bound (UCB)/163 Upper Confidence Bound (UCB) Intuition.mp4 29MB
  78. 31 Artificial Neural Networks/216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras.mp4 29MB
  79. 15 Kernel SVM/101 The Kernel Trick.mp4 29MB
  80. 12 Logistic Regression/077 Logistic Regression Intuition.mp4 29MB
  81. 34 Principal Component Analysis (PCA)/257 PCA in R - Step 2.mp4 29MB
  82. 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound in R - Step 2.mp4 29MB
  83. 21 K-Means Clustering/133 K-Means Clustering in R.mp4 29MB
  84. 29 -------------------- Part 7_ Natural Language Processing --------------------/201 Natural Language Processing in R - Step 9.mp4 29MB
  85. 31 Artificial Neural Networks/228 ANN in R - Step 3.mp4 29MB
  86. 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 5.mp4 29MB
  87. 27 Upper Confidence Bound (UCB)/169 Upper Confidence Bound in R - Step 1.mp4 28MB
  88. 16 Naive Bayes/107 Naive Bayes Intuition.mp4 28MB
  89. 06 Polynomial Regression/054 Python Regression Template.mp4 27MB
  90. 32 Convolutional Neural Networks/243 CNN in Python - Step 4.mp4 27MB
  91. 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27MB
  92. 06 Polynomial Regression/051 Polynomial Regression in Python - Step 2.mp4 27MB
  93. 24 Apriori/157 Apriori in Python - Step 3.mp4 27MB
  94. 21 K-Means Clustering/128 K-Means Clustering Intuition.mp4 27MB
  95. 31 Artificial Neural Networks/210 How do Neural Networks learn_.mp4 27MB
  96. 05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 2.mp4 26MB
  97. 07 Support Vector Regression (SVR)/062 SVR in R.mp4 26MB
  98. 34 Principal Component Analysis (PCA)/255 PCA in Python - Step 3.mp4 26MB
  99. 06 Polynomial Regression/059 R Regression Template.mp4 25MB
  100. 32 Convolutional Neural Networks/240 CNN in Python - Step 1.mp4 25MB
  101. 06 Polynomial Regression/050 Polynomial Regression in Python - Step 1.mp4 25MB
  102. 10 Evaluating Regression Models Performance/074 Interpreting Linear Regression Coefficients.mp4 24MB
  103. 29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10.mp4 24MB
  104. 29 -------------------- Part 7_ Natural Language Processing --------------------/185 Natural Language Processing in Python - Step 4.mp4 24MB
  105. 06 Polynomial Regression/056 Polynomial Regression in R - Step 2.mp4 24MB
  106. 31 Artificial Neural Networks/209 How do Neural Networks work_.mp4 24MB
  107. 16 Naive Bayes/111 Naive Bayes in Python.mp4 23MB
  108. 02 -------------------- Part 1_ Data Preprocessing --------------------/010 Importing the Dataset.mp4 23MB
  109. 21 K-Means Clustering/130 K-Means Selecting The Number Of Clusters.mp4 23MB
  110. 22 Hierarchical Clustering/136 Hierarchical Clustering Using Dendrograms.mp4 23MB
  111. 08 Decision Tree Regression/063 Decision Tree Regression Intuition.mp4 23MB
  112. 06 Polynomial Regression/058 Polynomial Regression in R - Step 4.mp4 22MB
  113. 34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2.mp4 22MB
  114. 29 -------------------- Part 7_ Natural Language Processing --------------------/183 Natural Language Processing in Python - Step 2.mp4 22MB
  115. 10 Evaluating Regression Models Performance/073 Evaluating Regression Models Performance - Homework's Final Part.mp4 22MB
  116. 04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 1.mp4 22MB
  117. 39 XGBoost/273 XGBoost in Python - Step 1.mp4 21MB
  118. 02 -------------------- Part 1_ Data Preprocessing --------------------/008 Get the dataset.mp4 21MB
  119. 25 Eclat/160 Eclat in R.mp4 21MB
  120. 32 Convolutional Neural Networks/249 CNN in Python - Step 10.mp4 21MB
  121. 02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!.mp4 20MB
  122. 01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows).mp4 20MB
  123. 18 Random Forest Classification/117 Random Forest Classification Intuition.mp4 19MB
  124. 10 Evaluating Regression Models Performance/072 Adjusted R-Squared Intuition.mp4 19MB
  125. 16 Naive Bayes/109 Naive Bayes Intuition (Extras).mp4 19MB
  126. 17 Decision Tree Classification/113 Decision Tree Classification Intuition.mp4 19MB
  127. 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2.mp4 19MB
  128. 19 Evaluating Classification Models Performance/124 CAP Curve.mp4 19MB
  129. 31 Artificial Neural Networks/211 Gradient Descent.mp4 19MB
  130. 31 Artificial Neural Networks/223 ANN in Python - Step 8.mp4 18MB
  131. 14 Support Vector Machine (SVM)/095 SVM Intuition.mp4 18MB
  132. 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1.mp4 18MB
  133. 06 Polynomial Regression/055 Polynomial Regression in R - Step 1.mp4 18MB
  134. 01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows).mp4 18MB
  135. 29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2.mp4 17MB
  136. 22 Hierarchical Clustering/135 Hierarchical Clustering How Dendrograms Work.mp4 17MB
  137. 05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17MB
  138. 29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7.mp4 17MB
  139. 31 Artificial Neural Networks/225 ANN in Python - Step 10.mp4 17MB
  140. 31 Artificial Neural Networks/224 ANN in Python - Step 9.mp4 17MB
  141. 31 Artificial Neural Networks/212 Stochastic Gradient Descent.mp4 17MB
  142. 22 Hierarchical Clustering/134 Hierarchical Clustering Intuition.mp4 17MB
  143. 31 Artificial Neural Networks/215 Business Problem Description.mp4 16MB
  144. 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3.mp4 16MB
  145. 21 K-Means Clustering/129 K-Means Random Initialization Trap.mp4 15MB
  146. 29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5.mp4 15MB
  147. 31 Artificial Neural Networks/208 The Activation Function.mp4 15MB
  148. 12 Logistic Regression/087 Logistic Regression in R - Step 3.mp4 15MB
  149. 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2.mp4 14MB
  150. 05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Step 3.mp4 14MB
  151. 05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 3.mp4 14MB
  152. 31 Artificial Neural Networks/227 ANN in R - Step 2.mp4 14MB
  153. 32 Convolutional Neural Networks/233 Step 1(b) - ReLU Layer.mp4 14MB
  154. 28 Thompson Sampling/174 Algorithm Comparison_ UCB vs Thompson Sampling.mp4 14MB
  155. 29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9.mp4 14MB
  156. 09 Random Forest Regression/067 Random Forest Regression Intuition.mp4 14MB
  157. 15 Kernel SVM/100 Mapping to a higher dimension.mp4 14MB
  158. 19 Evaluating Classification Models Performance/121 False Positives & False Negatives.mp4 14MB
  159. 29 -------------------- Part 7_ Natural Language Processing --------------------/195 Natural Language Processing in R - Step 3.mp4 14MB
  160. 06 Polynomial Regression/053 Polynomial Regression in Python - Step 4.mp4 14MB
  161. 16 Naive Bayes/108 Naive Bayes Intuition (Challenge Reveal).mp4 13MB
  162. 29 -------------------- Part 7_ Natural Language Processing --------------------/200 Natural Language Processing in R - Step 8.mp4 13MB
  163. 32 Convolutional Neural Networks/246 CNN in Python - Step 7.mp4 13MB
  164. 12 Logistic Regression/079 Logistic Regression in Python - Step 1.mp4 13MB
  165. 01 Welcome to the course!/002 Why Machine Learning is the Future.mp4 13MB
  166. 29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6.mp4 13MB
  167. 22 Hierarchical Clustering/139 HC in Python - Step 2.mp4 13MB
  168. 12 Logistic Regression/085 Logistic Regression in R - Step 1.mp4 13MB
  169. 12 Logistic Regression/090 R Classification Template.mp4 12MB
  170. 15 Kernel SVM/102 Types of Kernel Functions.mp4 12MB
  171. 22 Hierarchical Clustering/140 HC in Python - Step 3.mp4 12MB
  172. 12 Logistic Regression/084 Python Classification Template.mp4 12MB
  173. 22 Hierarchical Clustering/141 HC in Python - Step 4.mp4 12MB
  174. 04 Simple Linear Regression/018 How to get the dataset.mp4 12MB
  175. 05 Multiple Linear Regression/030 How to get the dataset.mp4 12MB
  176. 06 Polynomial Regression/049 How to get the dataset.mp4 12MB
  177. 07 Support Vector Regression (SVR)/060 How to get the dataset.mp4 12MB
  178. 08 Decision Tree Regression/064 How to get the dataset.mp4 12MB
  179. 09 Random Forest Regression/068 How to get the dataset.mp4 12MB
  180. 12 Logistic Regression/078 How to get the dataset.mp4 12MB
  181. 13 K-Nearest Neighbors (K-NN)/092 How to get the dataset.mp4 12MB
  182. 14 Support Vector Machine (SVM)/096 How to get the dataset.mp4 12MB
  183. 15 Kernel SVM/103 How to get the dataset.mp4 12MB
  184. 16 Naive Bayes/110 How to get the dataset.mp4 12MB
  185. 17 Decision Tree Classification/114 How to get the dataset.mp4 12MB
  186. 18 Random Forest Classification/118 How to get the dataset.mp4 12MB
  187. 21 K-Means Clustering/131 How to get the dataset.mp4 12MB
  188. 22 Hierarchical Clustering/137 How to get the dataset.mp4 12MB
  189. 24 Apriori/151 How to get the dataset.mp4 12MB
  190. 25 Eclat/159 How to get the dataset.mp4 12MB
  191. 27 Upper Confidence Bound (UCB)/164 How to get the dataset.mp4 12MB
  192. 28 Thompson Sampling/175 How to get the dataset.mp4 12MB
  193. 29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset.mp4 12MB
  194. 31 Artificial Neural Networks/214 How to get the dataset.mp4 12MB
  195. 32 Convolutional Neural Networks/239 How to get the dataset.mp4 12MB
  196. 34 Principal Component Analysis (PCA)/252 How to get the dataset.mp4 12MB
  197. 35 Linear Discriminant Analysis (LDA)/259 How to get the dataset.mp4 12MB
  198. 36 Kernel PCA/262 How to get the dataset.mp4 12MB
  199. 38 Model Selection/266 How to get the dataset.mp4 12MB
  200. 39 XGBoost/272 How to get the dataset.mp4 12MB
  201. 19 Evaluating Classification Models Performance/125 CAP Curve Analysis.mp4 12MB
  202. 22 Hierarchical Clustering/144 HC in R - Step 2.mp4 11MB
  203. 02 -------------------- Part 1_ Data Preprocessing --------------------/009 Importing the Libraries.mp4 11MB
  204. 31 Artificial Neural Networks/213 Backpropagation.mp4 11MB
  205. 22 Hierarchical Clustering/138 HC in Python - Step 1.mp4 11MB
  206. 25 Eclat/158 Eclat Intuition.mp4 11MB
  207. 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3.mp4 10MB
  208. 12 Logistic Regression/082 Logistic Regression in Python - Step 4.mp4 10MB
  209. 05 Multiple Linear Regression/031 Dataset + Business Problem Description.mp4 10MB
  210. 32 Convolutional Neural Networks/244 CNN in Python - Step 5.mp4 10MB
  211. 32 Convolutional Neural Networks/245 CNN in Python - Step 6.mp4 10MB
  212. 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1.mp4 10MB
  213. 04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 1.mp4 9MB
  214. 06 Polynomial Regression/048 Polynomial Regression Intuition.mp4 9MB
  215. 13 K-Nearest Neighbors (K-NN)/091 K-Nearest Neighbor Intuition.mp4 9MB
  216. 27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4.mp4 9MB
  217. 31 Artificial Neural Networks/222 ANN in Python - Step 7.mp4 9MB
  218. 10 Evaluating Regression Models Performance/071 R-Squared Intuition.mp4 9MB
  219. 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3.mp4 9MB
  220. 28 Thompson Sampling/177 Thompson Sampling in Python - Step 2.mp4 8MB
  221. 22 Hierarchical Clustering/142 HC in Python - Step 5.mp4 8MB
  222. 31 Artificial Neural Networks/218 ANN in Python - Step 3.mp4 8MB
  223. 12 Logistic Regression/080 Logistic Regression in Python - Step 2.mp4 8MB
  224. 19 Evaluating Classification Models Performance/122 Confusion Matrix.mp4 8MB
  225. 01 Welcome to the course!/001 Applications of Machine Learning.mp4 8MB
  226. 32 Convolutional Neural Networks/237 Summary.mp4 8MB
  227. 12 Logistic Regression/086 Logistic Regression in R - Step 2.mp4 8MB
  228. 22 Hierarchical Clustering/145 HC in R - Step 3.mp4 8MB
  229. 29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7.mp4 8MB
  230. 28 Thompson Sampling/179 Thompson Sampling in R - Step 2.mp4 7MB
  231. 22 Hierarchical Clustering/146 HC in R - Step 4.mp4 7MB
  232. 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in R - Step 4.mp4 7MB
  233. 22 Hierarchical Clustering/143 HC in R - Step 1.mp4 7MB
  234. 05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 2.mp4 7MB
  235. 31 Artificial Neural Networks/221 ANN in Python - Step 6.mp4 7MB
  236. 12 Logistic Regression/088 Logistic Regression in R - Step 4.mp4 7MB
  237. 22 Hierarchical Clustering/147 HC in R - Step 5.mp4 7MB
  238. 32 Convolutional Neural Networks/247 CNN in Python - Step 8.mp4 7MB
  239. 04 Simple Linear Regression/019 Dataset + Business Problem Description.mp4 7MB
  240. 29 -------------------- Part 7_ Natural Language Processing --------------------/196 Natural Language Processing in R - Step 4.mp4 6MB
  241. 29 -------------------- Part 7_ Natural Language Processing --------------------/187 Natural Language Processing in Python - Step 6.mp4 6MB
  242. 12 Logistic Regression/081 Logistic Regression in Python - Step 3.mp4 6MB
  243. 32 Convolutional Neural Networks/230 Plan of attack.mp4 6MB
  244. 31 Artificial Neural Networks/219 ANN in Python - Step 4.mp4 6MB
  245. 32 Convolutional Neural Networks/241 CNN in Python - Step 2.mp4 6MB
  246. 15 Kernel SVM/099 Kernel SVM Intuition.mp4 6MB
  247. 04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2.mp4 5MB
  248. 31 Artificial Neural Networks/206 Plan of attack.mp4 5MB
  249. 29 -------------------- Part 7_ Natural Language Processing --------------------/197 Natural Language Processing in R - Step 5.mp4 5MB
  250. 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 4.mp4 5MB
  251. 19 Evaluating Classification Models Performance/123 Accuracy Paradox.mp4 4MB
  252. 29 -------------------- Part 7_ Natural Language Processing --------------------/184 Natural Language Processing in Python - Step 3.mp4 3MB
  253. 32 Convolutional Neural Networks/235 Step 3 - Flattening.mp4 3MB
  254. 02 -------------------- Part 1_ Data Preprocessing --------------------/007 Welcome to Part 1 - Data Preprocessing.mp4 3MB
  255. 32 Convolutional Neural Networks/242 CNN in Python - Step 3.mp4 2MB
  256. 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1.mp4 2MB
  257. 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2.mp4 2MB
  258. 25 Eclat/160 Eclat.zip 49KB
  259. 16 Naive Bayes/106 Bayes Theorem-subtitle-en.srt 34KB
  260. 18 Random Forest Classification/120 Random Forest Classification in R-subtitle-en.srt 32KB
  261. 08 Decision Tree Regression/066 Decision Tree Regression in R-subtitle-en.srt 32KB
  262. 36 Kernel PCA/264 Kernel PCA in R-subtitle-en.srt 32KB
  263. 06 Polynomial Regression/052 Polynomial Regression in Python - Step 3-subtitle-en.srt 31KB
  264. 24 Apriori/154 Apriori in R - Step 3-subtitle-en.srt 31KB
  265. 24 Apriori/152 Apriori in R - Step 1-subtitle-en.srt 31KB
  266. 07 Support Vector Regression (SVR)/061 SVR in Python-subtitle-en.srt 31KB
  267. 35 Linear Discriminant Analysis (LDA)/261 LDA in R-subtitle-en.srt 31KB
  268. 06 Polynomial Regression/057 Polynomial Regression in R - Step 3-subtitle-en.srt 31KB
  269. 18 Random Forest Classification/119 Random Forest Classification in Python-subtitle-en.srt 31KB
  270. 32 Convolutional Neural Networks/248 CNN in Python - Step 9-subtitle-en.srt 31KB
  271. 31 Artificial Neural Networks/217 ANN in Python - Step 2-subtitle-en.srt 30KB
  272. 28 Thompson Sampling/176 Thompson Sampling in Python - Step 1-subtitle-en.srt 30KB
  273. 12 Logistic Regression/083 Logistic Regression in Python - Step 5-subtitle-en.srt 30KB
  274. 32 Convolutional Neural Networks/236 Step 4 - Full Connection-subtitle-en.srt 30KB
  275. 17 Decision Tree Classification/116 Decision Tree Classification in R-subtitle-en.srt 29KB
  276. 12 Logistic Regression/089 Logistic Regression in R - Step 5-subtitle-en.srt 29KB
  277. 38 Model Selection/268 k-Fold Cross Validation in R-subtitle-en.srt 29KB
  278. 28 Thompson Sampling/178 Thompson Sampling in R - Step 1-subtitle-en.srt 29KB
  279. 28 Thompson Sampling/173 Thompson Sampling Intuition-subtitle-en.srt 29KB
  280. 15 Kernel SVM/104 Kernel SVM in Python-subtitle-en.srt 28KB
  281. 21 K-Means Clustering/132 K-Means Clustering in Python-subtitle-en.srt 28KB
  282. 09 Random Forest Regression/070 Random Forest Regression in R-subtitle-en.srt 28KB
  283. 27 Upper Confidence Bound (UCB)/167 Upper Confidence Bound in Python - Step 3-subtitle-en.srt 28KB
  284. 24 Apriori/155 Apriori in Python - Step 1-subtitle-en.srt 28KB
  285. 31 Artificial Neural Networks/226 ANN in R - Step 1-subtitle-en.srt 28KB
  286. 09 Random Forest Regression/069 Random Forest Regression in Python-subtitle-en.srt 28KB
  287. 35 Linear Discriminant Analysis (LDA)/260 LDA in Python-subtitle-en.srt 28KB
  288. 05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !-subtitle-en.srt 27KB
  289. 29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10-subtitle-en.srt 27KB
  290. 39 XGBoost/275 XGBoost in R-subtitle-en.srt 27KB
  291. 02 -------------------- Part 1_ Data Preprocessing --------------------/013 Categorical Data-subtitle-en.srt 27KB
  292. 02 -------------------- Part 1_ Data Preprocessing --------------------/014 Splitting the Dataset into the Training set and Test set-subtitle-en.srt 27KB
  293. 24 Apriori/150 Apriori Intuition-subtitle-en.srt 27KB
  294. 27 Upper Confidence Bound (UCB)/171 Upper Confidence Bound in R - Step 3-subtitle-en.srt 26KB
  295. 27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2-subtitle-en.srt 26KB
  296. 32 Convolutional Neural Networks/238 Softmax & Cross-Entropy-subtitle-en.srt 26KB
  297. 31 Artificial Neural Networks/207 The Neuron-subtitle-en.srt 26KB
  298. 15 Kernel SVM/105 Kernel SVM in R-subtitle-en.srt 25KB
  299. 29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1-subtitle-en.srt 25KB
  300. 12 Logistic Regression/077 Logistic Regression Intuition-subtitle-en.srt 25KB
  301. 29 -------------------- Part 7_ Natural Language Processing --------------------/189 Natural Language Processing in Python - Step 8-subtitle-en.srt 25KB
  302. 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1-subtitle-en.srt 24KB
  303. 32 Convolutional Neural Networks/232 Step 1 - Convolution Operation-subtitle-en.srt 24KB
  304. 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4-subtitle-en.srt 24KB
  305. 08 Decision Tree Regression/065 Decision Tree Regression in Python-subtitle-en.srt 24KB
  306. 02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data-subtitle-en.srt 24KB
  307. 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 5-subtitle-en.srt 24KB
  308. 02 -------------------- Part 1_ Data Preprocessing --------------------/015 Feature Scaling-subtitle-en.srt 23KB
  309. 13 K-Nearest Neighbors (K-NN)/094 K-NN in R-subtitle-en.srt 23KB
  310. 21 K-Means Clustering/128 K-Means Clustering Intuition-subtitle-en.srt 23KB
  311. 16 Naive Bayes/107 Naive Bayes Intuition-subtitle-en.srt 23KB
  312. 27 Upper Confidence Bound (UCB)/162 The Multi-Armed Bandit Problem-subtitle-en.srt 23KB
  313. 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound in R - Step 2-subtitle-en.srt 23KB
  314. 24 Apriori/153 Apriori in R - Step 2-subtitle-en.srt 23KB
  315. 38 Model Selection/269 Grid Search in Python - Step 1-subtitle-en.srt 23KB
  316. 32 Convolutional Neural Networks/231 What are convolutional neural networks_-subtitle-en.srt 23KB
  317. 27 Upper Confidence Bound (UCB)/163 Upper Confidence Bound (UCB) Intuition-subtitle-en.srt 23KB
  318. 27 Upper Confidence Bound (UCB)/165 Upper Confidence Bound in Python - Step 1-subtitle-en.srt 23KB
  319. 24 Apriori/156 Apriori in Python - Step 2-subtitle-en.srt 23KB
  320. 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 4-subtitle-en.srt 22KB
  321. 36 Kernel PCA/263 Kernel PCA in Python-subtitle-en.srt 22KB
  322. 16 Naive Bayes/112 Naive Bayes in R-subtitle-en.srt 22KB
  323. 32 Convolutional Neural Networks/234 Step 2 - Pooling-subtitle-en.srt 22KB
  324. 38 Model Selection/271 Grid Search in R-subtitle-en.srt 22KB
  325. 31 Artificial Neural Networks/229 ANN in R - Step 4 (Last step)-subtitle-en.srt 21KB
  326. 27 Upper Confidence Bound (UCB)/169 Upper Confidence Bound in R - Step 1-subtitle-en.srt 21KB
  327. 13 K-Nearest Neighbors (K-NN)/093 K-NN in Python-subtitle-en.srt 21KB
  328. 38 Model Selection/267 k-Fold Cross Validation in Python-subtitle-en.srt 21KB
  329. 31 Artificial Neural Networks/216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras-subtitle-en.srt 21KB
  330. 34 Principal Component Analysis (PCA)/258 PCA in R - Step 3-subtitle-en.srt 21KB
  331. 29 -------------------- Part 7_ Natural Language Processing --------------------/201 Natural Language Processing in R - Step 9-subtitle-en.srt 20KB
  332. 31 Artificial Neural Networks/220 ANN in Python - Step 5-subtitle-en.srt 20KB
  333. 32 Convolutional Neural Networks/243 CNN in Python - Step 4-subtitle-en.srt 20KB
  334. 31 Artificial Neural Networks/209 How do Neural Networks work_-subtitle-en.srt 20KB
  335. 05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - Preparation-subtitle-en.srt 20KB
  336. 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !-subtitle-en.srt 20KB
  337. 31 Artificial Neural Networks/210 How do Neural Networks learn_-subtitle-en.srt 20KB
  338. 39 XGBoost/274 XGBoost in Python - Step 2-subtitle-en.srt 20KB
  339. 31 Artificial Neural Networks/228 ANN in R - Step 3-subtitle-en.srt 20KB
  340. 24 Apriori/157 Apriori in Python - Step 3-subtitle-en.srt 20KB
  341. 21 K-Means Clustering/133 K-Means Clustering in R-subtitle-en.srt 19KB
  342. 17 Decision Tree Classification/115 Decision Tree Classification in Python-subtitle-en.srt 19KB
  343. 34 Principal Component Analysis (PCA)/256 PCA in R - Step 1-subtitle-en.srt 19KB
  344. 14 Support Vector Machine (SVM)/097 SVM in Python-subtitle-en.srt 19KB
  345. 29 -------------------- Part 7_ Natural Language Processing --------------------/182 Natural Language Processing in Python - Step 1-subtitle-en.srt 19KB
  346. 32 Convolutional Neural Networks/240 CNN in Python - Step 1-subtitle-en.srt 19KB
  347. 30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_-subtitle-en.srt 19KB
  348. 07 Support Vector Regression (SVR)/062 SVR in R-subtitle-en.srt 19KB
  349. 02 -------------------- Part 1_ Data Preprocessing --------------------/010 Importing the Dataset-subtitle-en.srt 19KB
  350. 06 Polynomial Regression/059 R Regression Template-subtitle-en.srt 19KB
  351. 21 K-Means Clustering/130 K-Means Selecting The Number Of Clusters-subtitle-en.srt 18KB
  352. 14 Support Vector Machine (SVM)/098 SVM in R-subtitle-en.srt 18KB
  353. 34 Principal Component Analysis (PCA)/253 PCA in Python - Step 1-subtitle-en.srt 18KB
  354. 29 -------------------- Part 7_ Natural Language Processing --------------------/185 Natural Language Processing in Python - Step 4-subtitle-en.srt 18KB
  355. 22 Hierarchical Clustering/136 Hierarchical Clustering Using Dendrograms-subtitle-en.srt 18KB
  356. 34 Principal Component Analysis (PCA)/257 PCA in R - Step 2-subtitle-en.srt 18KB
  357. 06 Polynomial Regression/050 Polynomial Regression in Python - Step 1-subtitle-en.srt 17KB
  358. 06 Polynomial Regression/051 Polynomial Regression in Python - Step 2-subtitle-en.srt 17KB
  359. 15 Kernel SVM/101 The Kernel Trick-subtitle-en.srt 17KB
  360. 08 Decision Tree Regression/063 Decision Tree Regression Intuition-subtitle-en.srt 17KB
  361. 29 -------------------- Part 7_ Natural Language Processing --------------------/183 Natural Language Processing in Python - Step 2-subtitle-en.srt 16KB
  362. 06 Polynomial Regression/054 Python Regression Template-subtitle-en.srt 16KB
  363. 19 Evaluating Classification Models Performance/124 CAP Curve-subtitle-en.srt 16KB
  364. 38 Model Selection/270 Grid Search in Python - Step 2-subtitle-en.srt 16KB
  365. 16 Naive Bayes/109 Naive Bayes Intuition (Extras)-subtitle-en.srt 16KB
  366. 25 Eclat/160 Eclat in R-subtitle-en.srt 16KB
  367. 14 Support Vector Machine (SVM)/095 SVM Intuition-subtitle-en.srt 16KB
  368. 04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 1-subtitle-en.srt 15KB
  369. 06 Polynomial Regression/058 Polynomial Regression in R - Step 4-subtitle-en.srt 15KB
  370. 05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 2-subtitle-en.srt 15KB
  371. 34 Principal Component Analysis (PCA)/255 PCA in Python - Step 3-subtitle-en.srt 15KB
  372. 06 Polynomial Regression/056 Polynomial Regression in R - Step 2-subtitle-en.srt 15KB
  373. 29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10-subtitle-en.srt 15KB
  374. 22 Hierarchical Clustering/134 Hierarchical Clustering Intuition-subtitle-en.srt 15KB
  375. 31 Artificial Neural Networks/211 Gradient Descent-subtitle-en.srt 15KB
  376. 10 Evaluating Regression Models Performance/072 Adjusted R-Squared Intuition-subtitle-en.srt 14KB
  377. 22 Hierarchical Clustering/135 Hierarchical Clustering How Dendrograms Work-subtitle-en.srt 14KB
  378. 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-subtitle-en.srt 14KB
  379. 02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!-subtitle-en.srt 14KB
  380. 39 XGBoost/273 XGBoost in Python - Step 1-subtitle-en.srt 14KB
  381. 06 Polynomial Regression/055 Polynomial Regression in R - Step 1-subtitle-en.srt 14KB
  382. 16 Naive Bayes/111 Naive Bayes in Python-subtitle-en.srt 14KB
  383. 32 Convolutional Neural Networks/249 CNN in Python - Step 10-subtitle-en.srt 13KB
  384. 29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2-subtitle-en.srt 13KB
  385. 10 Evaluating Regression Models Performance/074 Interpreting Linear Regression Coefficients-subtitle-en.srt 13KB
  386. 21 K-Means Clustering/129 K-Means Random Initialization Trap-subtitle-en.srt 13KB
  387. 10 Evaluating Regression Models Performance/073 Evaluating Regression Models Performance - Homework's Final Part-subtitle-en.srt 13KB
  388. 17 Decision Tree Classification/113 Decision Tree Classification Intuition-subtitle-en.srt 13KB
  389. 31 Artificial Neural Networks/212 Stochastic Gradient Descent-subtitle-en.srt 13KB
  390. 31 Artificial Neural Networks/208 The Activation Function-subtitle-en.srt 12KB
  391. 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2-subtitle-en.srt 12KB
  392. 01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows)-subtitle-en.srt 12KB
  393. 34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2-subtitle-en.srt 12KB
  394. 05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution-subtitle-en.srt 12KB
  395. 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1-subtitle-en.srt 12KB
  396. 28 Thompson Sampling/174 Algorithm Comparison_ UCB vs Thompson Sampling-subtitle-en.srt 12KB
  397. 31 Artificial Neural Networks/223 ANN in Python - Step 8-subtitle-en.srt 12KB
  398. 19 Evaluating Classification Models Performance/121 False Positives & False Negatives-subtitle-en.srt 11KB
  399. 29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5-subtitle-en.srt 11KB
  400. 02 -------------------- Part 1_ Data Preprocessing --------------------/008 Get the dataset-subtitle-en.srt 11KB
  401. 15 Kernel SVM/100 Mapping to a higher dimension-subtitle-en.srt 11KB
  402. 31 Artificial Neural Networks/225 ANN in Python - Step 10-subtitle-en.srt 11KB
  403. 05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 3-subtitle-en.srt 11KB
  404. 29 -------------------- Part 7_ Natural Language Processing --------------------/195 Natural Language Processing in R - Step 3-subtitle-en.srt 11KB
  405. 31 Artificial Neural Networks/227 ANN in R - Step 2-subtitle-en.srt 11KB
  406. 09 Random Forest Regression/067 Random Forest Regression Intuition-subtitle-en.srt 10KB
  407. 29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7-subtitle-en.srt 10KB
  408. 31 Artificial Neural Networks/224 ANN in Python - Step 9-subtitle-en.srt 10KB
  409. 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3-subtitle-en.srt 10KB
  410. 01 Welcome to the course!/002 Why Machine Learning is the Future-subtitle-en.srt 10KB
  411. 32 Convolutional Neural Networks/233 Step 1(b) - ReLU Layer-subtitle-en.srt 10KB
  412. 22 Hierarchical Clustering/139 HC in Python - Step 2-subtitle-en.srt 10KB
  413. 16 Naive Bayes/108 Naive Bayes Intuition (Challenge Reveal)-subtitle-en.srt 9KB
  414. 32 Convolutional Neural Networks/246 CNN in Python - Step 7-subtitle-en.srt 9KB
  415. 19 Evaluating Classification Models Performance/125 CAP Curve Analysis-subtitle-en.srt 9KB
  416. 01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows)-subtitle-en.srt 9KB
  417. 12 Logistic Regression/085 Logistic Regression in R - Step 1-subtitle-en.srt 9KB
  418. 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2-subtitle-en.srt 9KB
  419. 12 Logistic Regression/079 Logistic Regression in Python - Step 1-subtitle-en.srt 9KB
  420. 29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6-subtitle-en.srt 9KB
  421. 06 Polynomial Regression/053 Polynomial Regression in Python - Step 4-subtitle-en.srt 9KB
  422. 29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9-subtitle-en.srt 9KB
  423. 25 Eclat/158 Eclat Intuition-subtitle-en.srt 8KB
  424. 02 -------------------- Part 1_ Data Preprocessing --------------------/009 Importing the Libraries-subtitle-en.srt 8KB
  425. 29 -------------------- Part 7_ Natural Language Processing --------------------/200 Natural Language Processing in R - Step 8-subtitle-en.srt 8KB
  426. 04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 1-subtitle-en.srt 8KB
  427. 05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Step 3-subtitle-en.srt 8KB
  428. 14 Support Vector Machine (SVM)/098 SVM.zip 8KB
  429. 22 Hierarchical Clustering/144 HC in R - Step 2-subtitle-en.srt 8KB
  430. 13 K-Nearest Neighbors (K-NN)/091 K-Nearest Neighbor Intuition-subtitle-en.srt 8KB
  431. 32 Convolutional Neural Networks/245 CNN in Python - Step 6-subtitle-en.srt 8KB
  432. 06 Polynomial Regression/048 Polynomial Regression Intuition-subtitle-en.srt 8KB
  433. 32 Convolutional Neural Networks/244 CNN in Python - Step 5-subtitle-en.srt 8KB
  434. 22 Hierarchical Clustering/140 HC in Python - Step 3-subtitle-en.srt 8KB
  435. 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1-subtitle-en.srt 8KB
  436. 22 Hierarchical Clustering/138 HC in Python - Step 1-subtitle-en.srt 8KB
  437. 31 Artificial Neural Networks/215 Business Problem Description-subtitle-en.srt 8KB
  438. 19 Evaluating Classification Models Performance/122 Confusion Matrix-subtitle-en.srt 8KB
  439. 12 Logistic Regression/087 Logistic Regression in R - Step 3-subtitle-en.srt 7KB
  440. 31 Artificial Neural Networks/213 Backpropagation-subtitle-en.srt 7KB
  441. 10 Evaluating Regression Models Performance/071 R-Squared Intuition-subtitle-en.srt 7KB
  442. 12 Logistic Regression/082 Logistic Regression in Python - Step 4-subtitle-en.srt 7KB
  443. 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3-subtitle-en.srt 7KB
  444. 18 Random Forest Classification/117 Random Forest Classification Intuition-subtitle-en.srt 7KB
  445. 22 Hierarchical Clustering/142 HC in Python - Step 5-subtitle-en.srt 7KB
  446. 12 Logistic Regression/090 R Classification Template-subtitle-en.srt 7KB
  447. 22 Hierarchical Clustering/141 HC in Python - Step 4-subtitle-en.srt 6KB
  448. 22 Hierarchical Clustering/143 HC in R - Step 1-subtitle-en.srt 6KB
  449. 32 Convolutional Neural Networks/237 Summary-subtitle-en.srt 6KB
  450. 12 Logistic Regression/084 Python Classification Template-subtitle-en.srt 6KB
  451. 28 Thompson Sampling/177 Thompson Sampling in Python - Step 2-subtitle-en.srt 6KB
  452. 40 Bonus Lectures/276 ___YOUR SPECIAL BONUS___.html 6KB
  453. 31 Artificial Neural Networks/222 ANN in Python - Step 7-subtitle-en.srt 6KB
  454. 05 Multiple Linear Regression/031 Dataset + Business Problem Description-subtitle-en.srt 6KB
  455. 29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7-subtitle-en.srt 6KB
  456. 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3-subtitle-en.srt 6KB
  457. 01 Welcome to the course!/001 Applications of Machine Learning-subtitle-en.srt 6KB
  458. 32 Convolutional Neural Networks/230 Plan of attack-subtitle-en.srt 5KB
  459. 28 Thompson Sampling/179 Thompson Sampling in R - Step 2-subtitle-en.srt 5KB
  460. 31 Artificial Neural Networks/218 ANN in Python - Step 3-subtitle-en.srt 5KB
  461. 27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4-subtitle-en.srt 5KB
  462. 15 Kernel SVM/102 Types of Kernel Functions-subtitle-en.srt 5KB
  463. 04 Simple Linear Regression/018 How to get the dataset-subtitle-en.srt 5KB
  464. 05 Multiple Linear Regression/030 How to get the dataset-subtitle-en.srt 5KB
  465. 06 Polynomial Regression/049 How to get the dataset-subtitle-en.srt 5KB
  466. 07 Support Vector Regression (SVR)/060 How to get the dataset-subtitle-en.srt 5KB
  467. 08 Decision Tree Regression/064 How to get the dataset-subtitle-en.srt 5KB
  468. 09 Random Forest Regression/068 How to get the dataset-subtitle-en.srt 5KB
  469. 12 Logistic Regression/078 How to get the dataset-subtitle-en.srt 5KB
  470. 13 K-Nearest Neighbors (K-NN)/092 How to get the dataset-subtitle-en.srt 5KB
  471. 14 Support Vector Machine (SVM)/096 How to get the dataset-subtitle-en.srt 5KB
  472. 15 Kernel SVM/103 How to get the dataset-subtitle-en.srt 5KB
  473. 16 Naive Bayes/110 How to get the dataset-subtitle-en.srt 5KB
  474. 17 Decision Tree Classification/114 How to get the dataset-subtitle-en.srt 5KB
  475. 18 Random Forest Classification/118 How to get the dataset-subtitle-en.srt 5KB
  476. 21 K-Means Clustering/131 How to get the dataset-subtitle-en.srt 5KB
  477. 22 Hierarchical Clustering/137 How to get the dataset-subtitle-en.srt 5KB
  478. 24 Apriori/151 How to get the dataset-subtitle-en.srt 5KB
  479. 25 Eclat/159 How to get the dataset-subtitle-en.srt 5KB
  480. 27 Upper Confidence Bound (UCB)/164 How to get the dataset-subtitle-en.srt 5KB
  481. 28 Thompson Sampling/175 How to get the dataset-subtitle-en.srt 5KB
  482. 29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset-subtitle-en.srt 5KB
  483. 31 Artificial Neural Networks/214 How to get the dataset-subtitle-en.srt 5KB
  484. 32 Convolutional Neural Networks/239 How to get the dataset-subtitle-en.srt 5KB
  485. 34 Principal Component Analysis (PCA)/252 How to get the dataset-subtitle-en.srt 5KB
  486. 35 Linear Discriminant Analysis (LDA)/259 How to get the dataset-subtitle-en.srt 5KB
  487. 36 Kernel PCA/262 How to get the dataset-subtitle-en.srt 5KB
  488. 38 Model Selection/266 How to get the dataset-subtitle-en.srt 5KB
  489. 39 XGBoost/272 How to get the dataset-subtitle-en.srt 5KB
  490. 12 Logistic Regression/080 Logistic Regression in Python - Step 2-subtitle-en.srt 5KB
  491. 19 Evaluating Classification Models Performance/126 Conclusion of Part 3 - Classification.html 5KB
  492. 32 Convolutional Neural Networks/247 CNN in Python - Step 8-subtitle-en.srt 5KB
  493. 22 Hierarchical Clustering/145 HC in R - Step 3-subtitle-en.srt 5KB
  494. 29 -------------------- Part 7_ Natural Language Processing --------------------/196 Natural Language Processing in R - Step 4-subtitle-en.srt 5KB
  495. 32 Convolutional Neural Networks/241 CNN in Python - Step 2-subtitle-en.srt 5KB
  496. 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in R - Step 4-subtitle-en.srt 5KB
  497. 15 Kernel SVM/099 Kernel SVM Intuition-subtitle-en.srt 5KB
  498. 29 -------------------- Part 7_ Natural Language Processing --------------------/187 Natural Language Processing in Python - Step 6-subtitle-en.srt 5KB
  499. 31 Artificial Neural Networks/221 ANN in Python - Step 6-subtitle-en.srt 5KB
  500. 12 Logistic Regression/086 Logistic Regression in R - Step 2-subtitle-en.srt 4KB
  501. 04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2-subtitle-en.srt 4KB
  502. 10 Evaluating Regression Models Performance/075 Conclusion of Part 2 - Regression.html 4KB
  503. 31 Artificial Neural Networks/206 Plan of attack-subtitle-en.srt 4KB
  504. 12 Logistic Regression/081 Logistic Regression in Python - Step 3-subtitle-en.srt 4KB
  505. 04 Simple Linear Regression/019 Dataset + Business Problem Description-subtitle-en.srt 4KB
  506. 05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 2-subtitle-en.srt 4KB
  507. 22 Hierarchical Clustering/147 HC in R - Step 5-subtitle-en.srt 4KB
  508. 12 Logistic Regression/088 Logistic Regression in R - Step 4-subtitle-en.srt 4KB
  509. 31 Artificial Neural Networks/219 ANN in Python - Step 4-subtitle-en.srt 4KB
  510. 22 Hierarchical Clustering/146 HC in R - Step 4-subtitle-en.srt 4KB
  511. 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 4-subtitle-en.srt 4KB
  512. 32 Convolutional Neural Networks/250 CNN in R.html 3KB
  513. 29 -------------------- Part 7_ Natural Language Processing --------------------/197 Natural Language Processing in R - Step 5-subtitle-en.srt 3KB
  514. 19 Evaluating Classification Models Performance/123 Accuracy Paradox-subtitle-en.srt 3KB
  515. 29 -------------------- Part 7_ Natural Language Processing --------------------/180 Welcome to Part 7 - Natural Language Processing.html 3KB
  516. 32 Convolutional Neural Networks/235 Step 3 - Flattening-subtitle-en.srt 3KB
  517. 29 -------------------- Part 7_ Natural Language Processing --------------------/184 Natural Language Processing in Python - Step 3-subtitle-en.srt 3KB
  518. 02 -------------------- Part 1_ Data Preprocessing --------------------/011 For Python learners_ summary of Object-oriented programming_ classes & objects.html 3KB
  519. 02 -------------------- Part 1_ Data Preprocessing --------------------/007 Welcome to Part 1 - Data Preprocessing-subtitle-en.srt 3KB
  520. 29 -------------------- Part 7_ Natural Language Processing --------------------/203 Homework Challenge.html 2KB
  521. 29 -------------------- Part 7_ Natural Language Processing --------------------/192 Homework Challenge.html 2KB
  522. 33 -------------------- Part 9_ Dimensionality Reduction --------------------/251 Welcome to Part 9 - Dimensionality Reduction.html 2KB
  523. 01 Welcome to the course!/004 Update_ Recommended Anaconda Version.html 2KB
  524. 01 Welcome to the course!/006 BONUS_ Meet your instructors.html 2KB
  525. 37 -------------------- Part 10_ Model Selection & Boosting --------------------/265 Welcome to Part 10 - Model Selection & Boosting.html 2KB
  526. 03 -------------------- Part 2_ Regression --------------------/017 Welcome to Part 2 - Regression.html 2KB
  527. 30 -------------------- Part 8_ Deep Learning --------------------/204 Welcome to Part 8 - Deep Learning.html 2KB
  528. 11 -------------------- Part 3_ Classification --------------------/076 Welcome to Part 3 - Classification.html 2KB
  529. 26 -------------------- Part 6_ Reinforcement Learning --------------------/161 Welcome to Part 6 - Reinforcement Learning.html 2KB
  530. 20 -------------------- Part 4_ Clustering --------------------/127 Welcome to Part 4 - Clustering.html 2KB
  531. 32 Convolutional Neural Networks/242 CNN in Python - Step 3-subtitle-en.srt 2KB
  532. 22 Hierarchical Clustering/148 Conclusion of Part 4 - Clustering.html 2KB
  533. 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1-subtitle-en.srt 2KB
  534. 23 -------------------- Part 5_ Association Rule Learning --------------------/149 Welcome to Part 5 - Association Rule Learning.html 2KB
  535. 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2-subtitle-en.srt 1KB
  536. [Discuss.FreeTutorials.us].url 252B
  537. [FreeCoursesOnline.Us].url 123B
  538. [FreeTutorials.Us].url 119B