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[] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

  • 收录时间:2018-02-25 13:33:16
  • 文件大小:7GB
  • 下载次数:199
  • 最近下载:2021-01-23 06:09:17
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文件列表

  1. 12 Logistic Regression/088 Logistic Regression in R - Step 5.mp4 94MB
  2. 31 Artificial Neural Networks/216 ANN in Python - Step 2.mp4 85MB
  3. 17 Decision Tree Classification/115 Decision Tree Classification in R.mp4 68MB
  4. 14 Support Vector Machine SVM/097 SVM in R.mp4 65MB
  5. 18 Random Forest Classification/119 Random Forest Classification in R.mp4 64MB
  6. 32 Convolutional Neural Networks/247 CNN in Python - Step 9.mp4 62MB
  7. 18 Random Forest Classification/118 Random Forest Classification in Python.mp4 62MB
  8. 07 Support Vector Regression SVR/060 SVR in Python.mp4 60MB
  9. 05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp4 59MB
  10. 27 Upper Confidence Bound UCB/170 Upper Confidence Bound in R - Step 3.mp4 58MB
  11. 36 Kernel PCA/263 Kernel PCA in R.mp4 57MB
  12. 24 Apriori/153 Apriori in R - Step 3.mp4 57MB
  13. 08 Decision Tree Regression/065 Decision Tree Regression in R.mp4 56MB
  14. 13 K-Nearest Neighbors K-NN/093 K-NN in R.mp4 56MB
  15. 28 Thompson Sampling/175 Thompson Sampling in Python - Step 1.mp4 56MB
  16. 15 Kernel SVM/103 Kernel SVM in Python.mp4 55MB
  17. 06 Polynomial Regression/056 Polynomial Regression in R - Step 3.mp4 55MB
  18. 05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 55MB
  19. 06 Polynomial Regression/051 Polynomial Regression in Python - Step 3.mp4 55MB
  20. 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 54MB
  21. 29 --------------------- Part 7 Natural Language Processing ---------------------/201 Natural Language Processing in R - Step 10.mp4 54MB
  22. 27 Upper Confidence Bound UCB/166 Upper Confidence Bound in Python - Step 3.mp4 54MB
  23. 12 Logistic Regression/082 Logistic Regression in Python - Step 5.mp4 53MB
  24. 02 -------------------------- Part 1 Data Preprocessing --------------------------/012 Categorical Data.mp4 53MB
  25. 24 Apriori/151 Apriori in R - Step 1.mp4 53MB
  26. 15 Kernel SVM/104 Kernel SVM in R.mp4 53MB
  27. 09 Random Forest Regression/068 Random Forest Regression in Python.mp4 53MB
  28. 05 Multiple Linear Regression/036 Multiple Linear Regression in Python - Step 1.mp4 52MB
  29. 29 --------------------- Part 7 Natural Language Processing ---------------------/188 Natural Language Processing in Python - Step 8.mp4 52MB
  30. 09 Random Forest Regression/069 Random Forest Regression in R.mp4 52MB
  31. 35 Linear Discriminant Analysis LDA/260 LDA in R.mp4 51MB
  32. 29 --------------------- Part 7 Natural Language Processing ---------------------/192 Natural Language Processing in R - Step 1.mp4 51MB
  33. 28 Thompson Sampling/177 Thompson Sampling in R - Step 1.mp4 51MB
  34. 02 -------------------------- Part 1 Data Preprocessing --------------------------/013 Splitting the Dataset into the Training set and Test set.mp4 51MB
  35. 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 51MB
  36. 16 Naive Bayes/105 Bayes Theorem.mp4 50MB
  37. 31 Artificial Neural Networks/225 ANN in R - Step 1.mp4 50MB
  38. 21 K-Means Clustering/131 K-Means Clustering in Python.mp4 50MB
  39. 16 Naive Bayes/111 Naive Bayes in R.mp4 50MB
  40. 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 4.mp4 49MB
  41. 24 Apriori/154 Apriori in Python - Step 1.mp4 47MB
  42. 39 XGBoost/274 XGBoost in R.mp4 47MB
  43. 13 K-Nearest Neighbors K-NN/092 K-NN in Python.mp4 47MB
  44. 29 --------------------- Part 7 Natural Language Processing ---------------------/181 Natural Language Processing in Python - Step 1.mp4 46MB
  45. 35 Linear Discriminant Analysis LDA/259 LDA in Python.mp4 45MB
  46. 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 2.mp4 45MB
  47. 02 -------------------------- Part 1 Data Preprocessing --------------------------/014 Feature Scaling.mp4 45MB
  48. 27 Upper Confidence Bound UCB/165 Upper Confidence Bound in Python - Step 2.mp4 44MB
  49. 31 Artificial Neural Networks/228 ANN in R - Step 4 Last step.mp4 44MB
  50. 38 Model Selection/267 k-Fold Cross Validation in R.mp4 44MB
  51. 08 Decision Tree Regression/064 Decision Tree Regression in Python.mp4 43MB
  52. 32 Convolutional Neural Networks/235 Step 4 - Full Connection.mp4 43MB
  53. 14 Support Vector Machine SVM/096 SVM in Python.mp4 42MB
  54. 32 Convolutional Neural Networks/233 Step 2 - Pooling.mp4 40MB
  55. 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 4.mp4 39MB
  56. 31 Artificial Neural Networks/219 ANN in Python - Step 5.mp4 39MB
  57. 02 -------------------------- Part 1 Data Preprocessing --------------------------/011 Missing Data.mp4 39MB
  58. 27 Upper Confidence Bound UCB/164 Upper Confidence Bound in Python - Step 1.mp4 39MB
  59. 17 Decision Tree Classification/114 Decision Tree Classification in Python.mp4 39MB
  60. 24 Apriori/152 Apriori in R - Step 2.mp4 39MB
  61. 38 Model Selection/268 Grid Search in Python - Step 1.mp4 38MB
  62. 31 Artificial Neural Networks/227 ANN in R - Step 3.mp4 38MB
  63. 29 --------------------- Part 7 Natural Language Processing ---------------------/200 Natural Language Processing in R - Step 9.mp4 38MB
  64. 31 Artificial Neural Networks/215 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp4 37MB
  65. 24 Apriori/155 Apriori in Python - Step 2.mp4 37MB
  66. 28 Thompson Sampling/172 Thompson Sampling Intuition.mp4 37MB
  67. 21 K-Means Clustering/132 K-Means Clustering in R.mp4 37MB
  68. 06 Polynomial Regression/053 Python Regression Template.mp4 37MB
  69. 34 Principal Component Analysis PCA/257 PCA in R - Step 3.mp4 37MB
  70. 38 Model Selection/270 Grid Search in R.mp4 36MB
  71. 24 Apriori/156 Apriori in Python - Step 3.mp4 35MB
  72. 06 Polynomial Regression/050 Polynomial Regression in Python - Step 2.mp4 35MB
  73. 24 Apriori/149 Apriori Intuition.mp4 35MB
  74. 15 Kernel SVM/100 The Kernel Trick.mp4 35MB
  75. 32 Convolutional Neural Networks/242 CNN in Python - Step 4.mp4 35MB
  76. 27 Upper Confidence Bound UCB/169 Upper Confidence Bound in R - Step 2.mp4 34MB
  77. 31 Artificial Neural Networks/222 ANN in Python - Step 8.mp4 34MB
  78. 27 Upper Confidence Bound UCB/168 Upper Confidence Bound in R - Step 1.mp4 34MB
  79. 07 Support Vector Regression SVR/061 SVR in R.mp4 34MB
  80. 36 Kernel PCA/262 Kernel PCA in Python.mp4 33MB
  81. 32 Convolutional Neural Networks/237 Softmax Cross-Entropy.mp4 33MB
  82. 29 --------------------- Part 7 Natural Language Processing ---------------------/190 Natural Language Processing in Python - Step 10.mp4 33MB
  83. 38 Model Selection/266 k-Fold Cross Validation in Python.mp4 33MB
  84. 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 5.mp4 33MB
  85. 06 Polynomial Regression/055 Polynomial Regression in R - Step 2.mp4 32MB
  86. 39 XGBoost/273 XGBoost in Python - Step 2.mp4 32MB
  87. 34 Principal Component Analysis PCA/252 PCA in Python - Step 1.mp4 32MB
  88. 06 Polynomial Regression/049 Polynomial Regression in Python - Step 1.mp4 32MB
  89. 06 Polynomial Regression/058 R Regression Template.mp4 31MB
  90. 30 ---------------------------- Part 8 Deep Learning ----------------------------/204 What is Deep Learning.mp4 31MB
  91. 16 Naive Bayes/110 Naive Bayes in Python.mp4 31MB
  92. 16 Naive Bayes/106 Naive Bayes Intuition.mp4 31MB
  93. 32 Convolutional Neural Networks/231 Step 1 - Convolution Operation.mp4 31MB
  94. 34 Principal Component Analysis PCA/255 PCA in R - Step 1.mp4 31MB
  95. 32 Convolutional Neural Networks/239 CNN in Python - Step 1.mp4 31MB
  96. 27 Upper Confidence Bound UCB/161 The Multi-Armed Bandit Problem.mp4 30MB
  97. 21 K-Means Clustering/127 K-Means Clustering Intuition.mp4 30MB
  98. 31 Artificial Neural Networks/206 The Neuron.mp4 30MB
  99. 29 --------------------- Part 7 Natural Language Processing ---------------------/184 Natural Language Processing in Python - Step 4.mp4 30MB
  100. 38 Model Selection/269 Grid Search in Python - Step 2.mp4 30MB
  101. 32 Convolutional Neural Networks/230 What are convolutional neural networks.mp4 29MB
  102. 27 Upper Confidence Bound UCB/162 Upper Confidence Bound UCB Intuition.mp4 29MB
  103. 31 Artificial Neural Networks/214 Business Problem Description.mp4 29MB
  104. 12 Logistic Regression/076 Logistic Regression Intuition.mp4 29MB
  105. 34 Principal Component Analysis PCA/256 PCA in R - Step 2.mp4 29MB
  106. 02 -------------------------- Part 1 Data Preprocessing --------------------------/009 Importing the Dataset.mp4 29MB
  107. 06 Polynomial Regression/057 Polynomial Regression in R - Step 4.mp4 29MB
  108. 31 Artificial Neural Networks/223 ANN in Python - Step 9.mp4 28MB
  109. 31 Artificial Neural Networks/224 ANN in Python - Step 10.mp4 28MB
  110. 10 Evaluating Regression Models Performance/072 Evaluating Regression Models Performance - Homeworks Final Part.mp4 28MB
  111. 04 Simple Linear Regression/021 Simple Linear Regression in Python - Step 1.mp4 28MB
  112. 32 Convolutional Neural Networks/248 CNN in Python - Step 10.mp4 28MB
  113. 12 Logistic Regression/086 Logistic Regression in R - Step 3.mp4 27MB
  114. 29 --------------------- Part 7 Natural Language Processing ---------------------/182 Natural Language Processing in Python - Step 2.mp4 27MB
  115. 10 Evaluating Regression Models Performance/073 Interpreting Linear Regression Coefficients.mp4 27MB
  116. 31 Artificial Neural Networks/209 How do Neural Networks learn.mp4 27MB
  117. 02 -------------------------- Part 1 Data Preprocessing --------------------------/015 And here is our Data Preprocessing Template.mp4 26MB
  118. 21 K-Means Clustering/129 K-Means Selecting The Number Of Clusters.mp4 26MB
  119. 18 Random Forest Classification/116 Random Forest Classification Intuition.mp4 26MB
  120. 34 Principal Component Analysis PCA/254 PCA in Python - Step 3.mp4 26MB
  121. 05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 3.mp4 25MB
  122. 08 Decision Tree Regression/062 Decision Tree Regression Intuition.mp4 25MB
  123. 25 Eclat/159 Eclat in R.mp4 25MB
  124. 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 2.mp4 25MB
  125. 04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 2.mp4 25MB
  126. 01 Welcome to the course/004 Installing Python and Anaconda MAC Windows.mp4 24MB
  127. 31 Artificial Neural Networks/208 How do Neural Networks work.mp4 24MB
  128. 05 Multiple Linear Regression/042 Multiple Linear Regression in R - Step 1.mp4 23MB
  129. 01 Welcome to the course/003 Installing R and R Studio MAC Windows.mp4 23MB
  130. 22 Hierarchical Clustering/135 Hierarchical Clustering Using Dendrograms.mp4 23MB
  131. 29 --------------------- Part 7 Natural Language Processing ---------------------/187 Natural Language Processing in Python - Step 7.mp4 22MB
  132. 34 Principal Component Analysis PCA/253 PCA in Python - Step 2.mp4 22MB
  133. 05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 22MB
  134. 29 --------------------- Part 7 Natural Language Processing ---------------------/193 Natural Language Processing in R - Step 2.mp4 22MB
  135. 17 Decision Tree Classification/112 Decision Tree Classification Intuition.mp4 22MB
  136. 10 Evaluating Regression Models Performance/071 Adjusted R-Squared Intuition.mp4 21MB
  137. 39 XGBoost/272 XGBoost in Python - Step 1.mp4 21MB
  138. 22 Hierarchical Clustering/140 HC in Python - Step 4.mp4 21MB
  139. 06 Polynomial Regression/054 Polynomial Regression in R - Step 1.mp4 21MB
  140. 02 -------------------------- Part 1 Data Preprocessing --------------------------/007 Get the dataset.mp4 21MB
  141. 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 3.mp4 21MB
  142. 19 Evaluating Classification Models Performance/123 CAP Curve.mp4 20MB
  143. 14 Support Vector Machine SVM/094 SVM Intuition.mp4 20MB
  144. 16 Naive Bayes/108 Naive Bayes Intuition Extras.mp4 19MB
  145. 29 --------------------- Part 7 Natural Language Processing ---------------------/189 Natural Language Processing in Python - Step 9.mp4 19MB
  146. 29 --------------------- Part 7 Natural Language Processing ---------------------/185 Natural Language Processing in Python - Step 5.mp4 19MB
  147. 31 Artificial Neural Networks/210 Gradient Descent.mp4 19MB
  148. 31 Artificial Neural Networks/226 ANN in R - Step 2.mp4 18MB
  149. 06 Polynomial Regression/052 Polynomial Regression in Python - Step 4.mp4 18MB
  150. 12 Logistic Regression/083 Python Classification Template.mp4 18MB
  151. 12 Logistic Regression/089 R Classification Template.mp4 18MB
  152. 22 Hierarchical Clustering/134 Hierarchical Clustering How Dendrograms Work.mp4 17MB
  153. 29 --------------------- Part 7 Natural Language Processing ---------------------/199 Natural Language Processing in R - Step 8.mp4 17MB
  154. 29 --------------------- Part 7 Natural Language Processing ---------------------/194 Natural Language Processing in R - Step 3.mp4 17MB
  155. 12 Logistic Regression/078 Logistic Regression in Python - Step 1.mp4 17MB
  156. 31 Artificial Neural Networks/211 Stochastic Gradient Descent.mp4 17MB
  157. 32 Convolutional Neural Networks/245 CNN in Python - Step 7.mp4 17MB
  158. 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 3.mp4 17MB
  159. 22 Hierarchical Clustering/133 Hierarchical Clustering Intuition.mp4 17MB
  160. 22 Hierarchical Clustering/139 HC in Python - Step 3.mp4 16MB
  161. 29 --------------------- Part 7 Natural Language Processing ---------------------/197 Natural Language Processing in R - Step 6.mp4 16MB
  162. 12 Logistic Regression/084 Logistic Regression in R - Step 1.mp4 16MB
  163. 15 Kernel SVM/101 Types of Kernel Functions.mp4 16MB
  164. 09 Random Forest Regression/066 Random Forest Regression Intuition.mp4 16MB
  165. 22 Hierarchical Clustering/138 HC in Python - Step 2.mp4 16MB
  166. 15 Kernel SVM/099 Mapping to a higher dimension.mp4 15MB
  167. 21 K-Means Clustering/128 K-Means Random Initialization Trap.mp4 15MB
  168. 19 Evaluating Classification Models Performance/120 False Positives False Negatives.mp4 15MB
  169. 31 Artificial Neural Networks/221 ANN in Python - Step 7.mp4 15MB
  170. 12 Logistic Regression/085 Logistic Regression in R - Step 2.mp4 15MB
  171. 31 Artificial Neural Networks/207 The Activation Function.mp4 15MB
  172. 31 Artificial Neural Networks/217 ANN in Python - Step 3.mp4 15MB
  173. 01 Welcome to the course/002 Why Machine Learning is the Future.mp4 14MB
  174. 32 Convolutional Neural Networks/232 Step 1b - ReLU Layer.mp4 14MB
  175. 28 Thompson Sampling/173 Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
  176. 12 Logistic Regression/081 Logistic Regression in Python - Step 4.mp4 14MB
  177. 22 Hierarchical Clustering/143 HC in R - Step 2.mp4 14MB
  178. 05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 3.mp4 14MB
  179. 22 Hierarchical Clustering/137 HC in Python - Step 1.mp4 14MB
  180. 22 Hierarchical Clustering/146 HC in R - Step 5.mp4 14MB
  181. 02 -------------------------- Part 1 Data Preprocessing --------------------------/008 Importing the Libraries.mp4 14MB
  182. 16 Naive Bayes/107 Naive Bayes Intuition Challenge Reveal.mp4 13MB
  183. 19 Evaluating Classification Models Performance/124 CAP Curve Analysis.mp4 13MB
  184. 05 Multiple Linear Regression/030 Dataset Business Problem Description.mp4 13MB
  185. 27 Upper Confidence Bound UCB/167 Upper Confidence Bound in Python - Step 4.mp4 12MB
  186. 32 Convolutional Neural Networks/243 CNN in Python - Step 5.mp4 12MB
  187. 32 Convolutional Neural Networks/244 CNN in Python - Step 6.mp4 12MB
  188. 31 Artificial Neural Networks/220 ANN in Python - Step 6.mp4 12MB
  189. 12 Logistic Regression/087 Logistic Regression in R - Step 4.mp4 12MB
  190. 04 Simple Linear Regression/017 How to get the dataset.mp4 12MB
  191. 05 Multiple Linear Regression/029 How to get the dataset.mp4 12MB
  192. 06 Polynomial Regression/048 How to get the dataset.mp4 12MB
  193. 07 Support Vector Regression SVR/059 How to get the dataset.mp4 12MB
  194. 08 Decision Tree Regression/063 How to get the dataset.mp4 12MB
  195. 09 Random Forest Regression/067 How to get the dataset.mp4 12MB
  196. 12 Logistic Regression/077 How to get the dataset.mp4 12MB
  197. 13 K-Nearest Neighbors K-NN/091 How to get the dataset.mp4 12MB
  198. 14 Support Vector Machine SVM/095 How to get the dataset.mp4 12MB
  199. 15 Kernel SVM/102 How to get the dataset.mp4 12MB
  200. 16 Naive Bayes/109 How to get the dataset.mp4 12MB
  201. 17 Decision Tree Classification/113 How to get the dataset.mp4 12MB
  202. 18 Random Forest Classification/117 How to get the dataset.mp4 12MB
  203. 21 K-Means Clustering/130 How to get the dataset.mp4 12MB
  204. 22 Hierarchical Clustering/136 How to get the dataset.mp4 12MB
  205. 24 Apriori/150 How to get the dataset.mp4 12MB
  206. 25 Eclat/158 How to get the dataset.mp4 12MB
  207. 27 Upper Confidence Bound UCB/163 How to get the dataset.mp4 12MB
  208. 28 Thompson Sampling/174 How to get the dataset.mp4 12MB
  209. 29 --------------------- Part 7 Natural Language Processing ---------------------/180 How to get the dataset.mp4 12MB
  210. 31 Artificial Neural Networks/213 How to get the dataset.mp4 12MB
  211. 32 Convolutional Neural Networks/238 How to get the dataset.mp4 12MB
  212. 34 Principal Component Analysis PCA/251 How to get the dataset.mp4 12MB
  213. 35 Linear Discriminant Analysis LDA/258 How to get the dataset.mp4 12MB
  214. 36 Kernel PCA/261 How to get the dataset.mp4 12MB
  215. 38 Model Selection/265 How to get the dataset.mp4 12MB
  216. 39 XGBoost/271 How to get the dataset.mp4 12MB
  217. 04 Simple Linear Regression/025 Simple Linear Regression in R - Step 1.mp4 12MB
  218. 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 3.mp4 11MB
  219. 28 Thompson Sampling/176 Thompson Sampling in Python - Step 2.mp4 11MB
  220. 12 Logistic Regression/079 Logistic Regression in Python - Step 2.mp4 11MB
  221. 31 Artificial Neural Networks/212 Backpropagation.mp4 11MB
  222. 25 Eclat/157 Eclat Intuition.mp4 11MB
  223. 04 Simple Linear Regression/019 Simple Linear Regression Intuition - Step 1.mp4 11MB
  224. 13 K-Nearest Neighbors K-NN/090 K-Nearest Neighbor Intuition.mp4 10MB
  225. 22 Hierarchical Clustering/145 HC in R - Step 4.mp4 10MB
  226. 22 Hierarchical Clustering/144 HC in R - Step 3.mp4 10MB
  227. 22 Hierarchical Clustering/141 HC in Python - Step 5.mp4 10MB
  228. 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 2.mp4 10MB
  229. 01 Welcome to the course/001 Applications of Machine Learning.mp4 10MB
  230. 10 Evaluating Regression Models Performance/070 R-Squared Intuition.mp4 10MB
  231. 31 Artificial Neural Networks/218 ANN in Python - Step 4.mp4 10MB
  232. 29 --------------------- Part 7 Natural Language Processing ---------------------/198 Natural Language Processing in R - Step 7.mp4 10MB
  233. 28 Thompson Sampling/178 Thompson Sampling in R - Step 2.mp4 10MB
  234. 27 Upper Confidence Bound UCB/171 Upper Confidence Bound in R - Step 4.mp4 10MB
  235. 06 Polynomial Regression/047 Polynomial Regression Intuition.mp4 9MB
  236. 32 Convolutional Neural Networks/246 CNN in Python - Step 8.mp4 9MB
  237. 19 Evaluating Classification Models Performance/121 Confusion Matrix.mp4 9MB
  238. 22 Hierarchical Clustering/142 HC in R - Step 1.mp4 9MB
  239. 29 --------------------- Part 7 Natural Language Processing ---------------------/186 Natural Language Processing in Python - Step 6.mp4 8MB
  240. 29 --------------------- Part 7 Natural Language Processing ---------------------/195 Natural Language Processing in R - Step 4.mp4 8MB
  241. 12 Logistic Regression/080 Logistic Regression in Python - Step 3.mp4 8MB
  242. 32 Convolutional Neural Networks/236 Summary.mp4 8MB
  243. 04 Simple Linear Regression/018 Dataset Business Problem Description.mp4 8MB
  244. 32 Convolutional Neural Networks/240 CNN in Python - Step 2.mp4 7MB
  245. 15 Kernel SVM/098 Kernel SVM Intuition.mp4 6MB
  246. 04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 2.mp4 6MB
  247. 32 Convolutional Neural Networks/229 Plan of attack.mp4 6MB
  248. 29 --------------------- Part 7 Natural Language Processing ---------------------/196 Natural Language Processing in R - Step 5.mp4 6MB
  249. 05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 4.mp4 5MB
  250. 31 Artificial Neural Networks/205 Plan of attack.mp4 5MB
  251. 19 Evaluating Classification Models Performance/122 Accuracy Paradox.mp4 4MB
  252. 29 --------------------- Part 7 Natural Language Processing ---------------------/183 Natural Language Processing in Python - Step 3.mp4 4MB
  253. 02 -------------------------- Part 1 Data Preprocessing --------------------------/006 Welcome to Part 1 - Data Preprocessing.mp4 4MB
  254. 32 Convolutional Neural Networks/234 Step 3 - Flattening.mp4 3MB
  255. 32 Convolutional Neural Networks/241 CNN in Python - Step 3.mp4 3MB
  256. 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 2.mp4 2MB
  257. 05 Multiple Linear Regression/031 Multiple Linear Regression Intuition - Step 1.mp4 2MB
  258. 25 Eclat/attached_files/159 Eclat in R/Eclat.zip 49KB
  259. 14 Support Vector Machine SVM/attached_files/097 SVM in R/SVM.zip 8KB
  260. 40 Bonus Lectures/275 YOUR SPECIAL BONUS.html 5KB
  261. 05 Multiple Linear Regression/quizzes/003 Multiple Linear Regression.html 5KB
  262. 02 -------------------------- Part 1 Data Preprocessing --------------------------/quizzes/001 Data Preprocessing.html 5KB
  263. 04 Simple Linear Regression/quizzes/002 Simple Linear Regression.html 4KB
  264. 22 Hierarchical Clustering/quizzes/007 Hierarchical Clustering.html 4KB
  265. 12 Logistic Regression/quizzes/004 Logistic Regression.html 4KB
  266. 21 K-Means Clustering/quizzes/006 K-Means Clustering.html 4KB
  267. 13 K-Nearest Neighbors K-NN/quizzes/005 K-Nearest Neighbor.html 4KB
  268. 19 Evaluating Classification Models Performance/125 Conclusion of Part 3 - Classification.html 4KB
  269. 10 Evaluating Regression Models Performance/074 Conclusion of Part 2 - Regression.html 3KB
  270. 32 Convolutional Neural Networks/249 CNN in R.html 3KB
  271. 29 --------------------- Part 7 Natural Language Processing ---------------------/179 Welcome to Part 7 - Natural Language Processing.html 2KB
  272. 29 --------------------- Part 7 Natural Language Processing ---------------------/202 Homework Challenge.html 2KB
  273. 29 --------------------- Part 7 Natural Language Processing ---------------------/191 Homework Challenge.html 2KB
  274. 33 ----------------------- Part 9 Dimensionality Reduction -----------------------/250 Welcome to Part 9 - Dimensionality Reduction.html 2KB
  275. 01 Welcome to the course/005 BONUS Meet your instructors.html 1KB
  276. 37 --------------------- Part 10 Model Selection Boosting ---------------------/264 Welcome to Part 10 - Model Selection Boosting.html 1KB
  277. 30 ---------------------------- Part 8 Deep Learning ----------------------------/203 Welcome to Part 8 - Deep Learning.html 1KB
  278. 03 ------------------------------ Part 2 Regression ------------------------------/016 Welcome to Part 2 - Regression.html 1KB
  279. 26 ------------------------ Part 6 Reinforcement Learning ------------------------/160 Welcome to Part 6 - Reinforcement Learning.html 1KB
  280. 11 ---------------------------- Part 3 Classification ----------------------------/075 Welcome to Part 3 - Classification.html 1KB
  281. [FreeCourseSite.com].txt 1KB
  282. [HaxTech.me].txt 1KB
  283. 20 ---------------------------- Part 4 Clustering ----------------------------/126 Welcome to Part 4 - Clustering.html 1004B
  284. 22 Hierarchical Clustering/147 Conclusion of Part 4 - Clustering.html 809B
  285. 23 ---------------------- Part 5 Association Rule Learning ----------------------/148 Welcome to Part 5 - Association Rule Learning.html 713B
  286. [FreeCourseSite.com].url 127B
  287. [HaxTech.me].url 123B