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UD424

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文件列表

  1. 37 Convolutional Neural Networks/289 Section-40-Convolutional-Neural-Networks-CNN.zip 224MB
  2. 29 Apriori/197 Apriori in Python - Step 4.mp4 164MB
  3. 37 Convolutional Neural Networks/295 CNN in Python - FINAL DEMO.mp4 153MB
  4. 43 Model Selection/315 Grid Search in Python.mp4 152MB
  5. 17 K-Nearest Neighbors (K-NN)/130 K-NN in Python.mp4 147MB
  6. 23 Classification Model Selection in Python/160 THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION.mp4 136MB
  7. 27 Hierarchical Clustering/183 Hierarchical Clustering in Python - Step 2.mp4 136MB
  8. 13 Regression Model Selection in Python/103 Preparation of the Regression Code Templates.mp4 124MB
  9. 26 K-Means Clustering/176 K-Means Clustering in Python - Step 5.mp4 121MB
  10. 16 Logistic Regression/118 Logistic Regression in Python - Step 7.mp4 119MB
  11. 37 Convolutional Neural Networks/292 CNN in Python - Step 3.mp4 119MB
  12. 39 Principal Component Analysis (PCA)/300 PCA in Python - Step 1.mp4 113MB
  13. 43 Model Selection/314 k-Fold Cross Validation in Python.mp4 112MB
  14. 36 Artificial Neural Networks/270 ANN in Python - Step 2.mp4 111MB
  15. 21 Decision Tree Classification/153 Decision Tree Classification in Python.mp4 108MB
  16. 29 Apriori/195 Apriori in Python - Step 2.mp4 108MB
  17. 37 Convolutional Neural Networks/291 CNN in Python - Step 2.mp4 107MB
  18. 18 Support Vector Machine (SVM)/134 SVM in Python.mp4 105MB
  19. 34 -------------------- Part 7 Natural Language Processing --------------------/234 Bag-Of-Words Model.mp4 103MB
  20. 40 Linear Discriminant Analysis (LDA)/307 LDA in Python.mp4 102MB
  21. 03 Data Preprocessing in Python/025 Feature Scaling.mp4 102MB
  22. 36 Artificial Neural Networks/273 ANN in Python - Step 5.mp4 101MB
  23. 20 Naive Bayes/149 Naive Bayes in Python.mp4 100MB
  24. 37 Convolutional Neural Networks/294 CNN in Python - Step 5.mp4 98MB
  25. 22 Random Forest Classification/157 Random Forest Classification in Python.mp4 97MB
  26. 01 Welcome to the course/010 Presentation of the ML A-Z folder Colaboratory Jupyter Notebook and Spyder.mp4 95MB
  27. 16 Logistic Regression/124 Logistic Regression in R - Step 5.mp4 94MB
  28. 09 Support Vector Regression (SVR)/087 SVR in Python - Step 5.mp4 94MB
  29. 44 XGBoost/319 XGBoost in Python.mp4 90MB
  30. 34 -------------------- Part 7 Natural Language Processing --------------------/240 Natural Language Processing in Python - Step 5.mp4 90MB
  31. 03 Data Preprocessing in Python/023 Encoding Categorical Data.mp4 89MB
  32. 19 Kernel SVM/142 Kernel SVM in Python.mp4 88MB
  33. 09 Support Vector Regression (SVR)/084 SVR in Python - Step 2.mp4 87MB
  34. 04 Data Preprocessing in R/033 Splitting the dataset into the Training set and Test set.mp4 86MB
  35. 32 Upper Confidence Bound (UCB)/212 Upper Confidence Bound in Python - Step 4.mp4 85MB
  36. 16 Logistic Regression/113 Logistic Regression in Python - Step 2.mp4 85MB
  37. 34 -------------------- Part 7 Natural Language Processing --------------------/233 Classical vs Deep Learning Models.mp4 84MB
  38. 26 K-Means Clustering/174 K-Means Clustering in Python - Step 3.mp4 81MB
  39. 04 Data Preprocessing in R/034 Feature Scaling.mp4 79MB
  40. 33 Thompson Sampling/225 Thompson Sampling in Python - Step 3.mp4 79MB
  41. 08 Polynomial Regression/073 Polynomial Regression in Python - Step 3.mp4 78MB
  42. 41 Kernel PCA/310 Kernel PCA in Python.mp4 77MB
  43. 30 Eclat/203 Eclat in Python.mp4 76MB
  44. 27 Hierarchical Clustering/184 Hierarchical Clustering in Python - Step 3.mp4 75MB
  45. 36 Artificial Neural Networks/271 ANN in Python - Step 3.mp4 75MB
  46. 06 Simple Linear Regression/043 Simple Linear Regression in Python - Step 4.mp4 75MB
  47. 11 Random Forest Regression/098 Random Forest Regression in Python.mp4 74MB
  48. 07 Multiple Linear Regression/060 Multiple Linear Regression in Python - Step 4.mp4 73MB
  49. 03 Data Preprocessing in Python/020 Importing the Dataset.mp4 72MB
  50. 37 Convolutional Neural Networks/290 CNN in Python - Step 1.mp4 71MB
  51. 33 Thompson Sampling/224 Thompson Sampling in Python - Step 2.mp4 70MB
  52. 29 Apriori/194 Apriori in Python - Step 1.mp4 70MB
  53. 08 Polynomial Regression/072 Polynomial Regression in Python - Step 2.mp4 69MB
  54. 29 Apriori/196 Apriori in Python - Step 3.mp4 69MB
  55. 03 Data Preprocessing in Python/022 Taking care of Missing Data.mp4 69MB
  56. 21 Decision Tree Classification/154 Decision Tree Classification in R.mp4 68MB
  57. 03 Data Preprocessing in Python/024 Splitting the dataset into the Training set and Test set.mp4 68MB
  58. 36 Artificial Neural Networks/268 ANN in Python - Step 1.mp4 66MB
  59. 19 Kernel SVM/140 Non-Linear Kernel SVR (Advanced).mp4 66MB
  60. 36 Artificial Neural Networks/272 ANN in Python - Step 4.mp4 65MB
  61. 18 Support Vector Machine (SVM)/135 SVM in R.mp4 65MB
  62. 22 Random Forest Classification/158 Random Forest Classification in R.mp4 64MB
  63. 07 Multiple Linear Regression/058 Multiple Linear Regression in Python - Step 2.mp4 62MB
  64. 34 -------------------- Part 7 Natural Language Processing --------------------/238 Natural Language Processing in Python - Step 3.mp4 61MB
  65. 34 -------------------- Part 7 Natural Language Processing --------------------/239 Natural Language Processing in Python - Step 4.mp4 60MB
  66. 32 Upper Confidence Bound (UCB)/209 Upper Confidence Bound in Python - Step 1.mp4 59MB
  67. 08 Polynomial Regression/071 Polynomial Regression in Python - Step 1.mp4 58MB
  68. 07 Multiple Linear Regression/059 Multiple Linear Regression in Python - Step 3.mp4 58MB
  69. 32 Upper Confidence Bound (UCB)/218 Upper Confidence Bound in R - Step 3.mp4 58MB
  70. 04 Data Preprocessing in R/032 Encoding Categorical Data.mp4 57MB
  71. 13 Regression Model Selection in Python/104 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION.mp4 57MB
  72. 41 Kernel PCA/311 Kernel PCA in R.mp4 57MB
  73. 29 Apriori/200 Apriori in R - Step 3.mp4 57MB
  74. 07 Multiple Linear Regression/054 Understanding the P-Value.mp4 56MB
  75. 10 Decision Tree Regression/095 Decision Tree Regression in R.mp4 56MB
  76. 17 K-Nearest Neighbors (K-NN)/131 K-NN in R.mp4 56MB
  77. 08 Polynomial Regression/077 Polynomial Regression in R - Step 3.mp4 55MB
  78. 10 Decision Tree Regression/094 Decision Tree Regression in Python - Step 4.mp4 55MB
  79. 03 Data Preprocessing in Python/018 Getting Started.mp4 54MB
  80. 34 -------------------- Part 7 Natural Language Processing --------------------/253 Natural Language Processing in R - Step 10.mp4 54MB
  81. 26 K-Means Clustering/173 K-Means Clustering in Python - Step 2.mp4 54MB
  82. 16 Logistic Regression/117 Logistic Regression in Python - Step 6.mp4 53MB
  83. 34 -------------------- Part 7 Natural Language Processing --------------------/241 Natural Language Processing in Python - Step 6.mp4 53MB
  84. 29 Apriori/198 Apriori in R - Step 1.mp4 53MB
  85. 19 Kernel SVM/143 Kernel SVM in R.mp4 53MB
  86. 44 XGBoost/322 THANK YOU bonus video.mp4 52MB
  87. 11 Random Forest Regression/099 Random Forest Regression in R.mp4 52MB
  88. 40 Linear Discriminant Analysis (LDA)/308 LDA in R.mp4 51MB
  89. 34 -------------------- Part 7 Natural Language Processing --------------------/244 Natural Language Processing in R - Step 1.mp4 51MB
  90. 33 Thompson Sampling/228 Thompson Sampling in R - Step 1.mp4 51MB
  91. 07 Multiple Linear Regression/057 Multiple Linear Regression in Python - Step 1.mp4 51MB
  92. 07 Multiple Linear Regression/066 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 51MB
  93. 04 Data Preprocessing in R/035 Data Preprocessing Template.mp4 51MB
  94. 20 Naive Bayes/144 Bayes Theorem.mp4 50MB
  95. 36 Artificial Neural Networks/274 ANN in R - Step 1.mp4 50MB
  96. 20 Naive Bayes/150 Naive Bayes in R.mp4 50MB
  97. 06 Simple Linear Regression/048 Simple Linear Regression in R - Step 4.mp4 49MB
  98. 06 Simple Linear Regression/040 Simple Linear Regression in Python - Step 1.mp4 49MB
  99. 44 XGBoost/321 XGBoost in R.mp4 47MB
  100. 09 Support Vector Regression (SVR)/086 SVR in Python - Step 4.mp4 46MB
  101. 07 Multiple Linear Regression/064 Multiple Linear Regression in R - Step 2.mp4 45MB
  102. 16 Logistic Regression/115 Logistic Regression in Python - Step 4.mp4 45MB
  103. 32 Upper Confidence Bound (UCB)/214 Upper Confidence Bound in Python - Step 6.mp4 45MB
  104. 33 Thompson Sampling/226 Thompson Sampling in Python - Step 4.mp4 45MB
  105. 16 Logistic Regression/112 Logistic Regression in Python - Step 1.mp4 45MB
  106. 36 Artificial Neural Networks/277 ANN in R - Step 4 (Last step).mp4 44MB
  107. 43 Model Selection/316 k-Fold Cross Validation in R.mp4 44MB
  108. 32 Upper Confidence Bound (UCB)/215 Upper Confidence Bound in Python - Step 7.mp4 43MB
  109. 16 Logistic Regression/114 Logistic Regression in Python - Step 3.mp4 43MB
  110. 37 Convolutional Neural Networks/286 Step 4 - Full Connection.mp4 43MB
  111. 09 Support Vector Regression (SVR)/083 SVR in Python - Step 1.mp4 43MB
  112. 10 Decision Tree Regression/091 Decision Tree Regression in Python - Step 1.mp4 42MB
  113. 39 Principal Component Analysis (PCA)/301 PCA in Python - Step 2.mp4 41MB
  114. 34 -------------------- Part 7 Natural Language Processing --------------------/237 Natural Language Processing in Python - Step 2.mp4 40MB
  115. 37 Convolutional Neural Networks/284 Step 2 - Pooling.mp4 40MB
  116. 27 Hierarchical Clustering/182 Hierarchical Clustering in Python - Step 1.mp4 40MB
  117. 37 Convolutional Neural Networks/293 CNN in Python - Step 4.mp4 40MB
  118. 06 Simple Linear Regression/041 Simple Linear Regression in Python - Step 2.mp4 40MB
  119. 04 Data Preprocessing in R/031 Taking care of Missing Data.mp4 40MB
  120. 29 Apriori/199 Apriori in R - Step 2.mp4 39MB
  121. 08 Polynomial Regression/074 Polynomial Regression in Python - Step 4.mp4 39MB
  122. 32 Upper Confidence Bound (UCB)/211 Upper Confidence Bound in Python - Step 3.mp4 38MB
  123. 26 K-Means Clustering/172 K-Means Clustering in Python - Step 1.mp4 38MB
  124. 36 Artificial Neural Networks/276 ANN in R - Step 3.mp4 38MB
  125. 34 -------------------- Part 7 Natural Language Processing --------------------/252 Natural Language Processing in R - Step 9.mp4 38MB
  126. 33 Thompson Sampling/220 Thompson Sampling Intuition.mp4 37MB
  127. 26 K-Means Clustering/177 K-Means Clustering in R.mp4 37MB
  128. 09 Support Vector Regression (SVR)/080 SVR Intuition (Updated).mp4 37MB
  129. 39 Principal Component Analysis (PCA)/304 PCA in R - Step 3.mp4 37MB
  130. 43 Model Selection/317 Grid Search in R.mp4 36MB
  131. 26 K-Means Clustering/175 K-Means Clustering in Python - Step 4.mp4 35MB
  132. 29 Apriori/192 Apriori Intuition.mp4 35MB
  133. 09 Support Vector Regression (SVR)/085 SVR in Python - Step 3.mp4 35MB
  134. 19 Kernel SVM/138 The Kernel Trick.mp4 35MB
  135. 32 Upper Confidence Bound (UCB)/217 Upper Confidence Bound in R - Step 2.mp4 34MB
  136. 34 -------------------- Part 7 Natural Language Processing --------------------/236 Natural Language Processing in Python - Step 1.mp4 34MB
  137. 32 Upper Confidence Bound (UCB)/216 Upper Confidence Bound in R - Step 1.mp4 34MB
  138. 09 Support Vector Regression (SVR)/088 SVR in R.mp4 34MB
  139. 37 Convolutional Neural Networks/288 Softmax Cross-Entropy.mp4 33MB
  140. 07 Multiple Linear Regression/055 Multiple Linear Regression Intuition - Step 5.mp4 33MB
  141. 32 Upper Confidence Bound (UCB)/213 Upper Confidence Bound in Python - Step 5.mp4 32MB
  142. 08 Polynomial Regression/076 Polynomial Regression in R - Step 2.mp4 32MB
  143. 39 Principal Component Analysis (PCA)/298 Principal Component Analysis (PCA) Intuition.mp4 32MB
  144. 08 Polynomial Regression/079 R Regression Template.mp4 31MB
  145. 35 -------------------- Part 8 Deep Learning --------------------/257 What is Deep Learning.mp4 31MB
  146. 20 Naive Bayes/145 Naive Bayes Intuition.mp4 31MB
  147. 37 Convolutional Neural Networks/282 Step 1 - Convolution Operation.mp4 31MB
  148. 39 Principal Component Analysis (PCA)/302 PCA in R - Step 1.mp4 31MB
  149. 16 Logistic Regression/116 Logistic Regression in Python - Step 5.mp4 31MB
  150. 33 Thompson Sampling/223 Thompson Sampling in Python - Step 1.mp4 31MB
  151. 32 Upper Confidence Bound (UCB)/206 The Multi-Armed Bandit Problem.mp4 30MB
  152. 26 K-Means Clustering/168 K-Means Clustering Intuition.mp4 30MB
  153. 36 Artificial Neural Networks/259 The Neuron.mp4 30MB
  154. 37 Convolutional Neural Networks/281 What are convolutional neural networks.mp4 29MB
  155. 32 Upper Confidence Bound (UCB)/207 Upper Confidence Bound (UCB) Intuition.mp4 29MB
  156. 36 Artificial Neural Networks/266 Business Problem Description.mp4 29MB
  157. 16 Logistic Regression/110 Logistic Regression Intuition.mp4 29MB
  158. 39 Principal Component Analysis (PCA)/303 PCA in R - Step 2.mp4 29MB
  159. 08 Polynomial Regression/078 Polynomial Regression in R - Step 4.mp4 29MB
  160. 14 Regression Model Selection in R/106 Evaluating Regression Models Performance - Homeworks Final Part.mp4 28MB
  161. 06 Simple Linear Regression/042 Simple Linear Regression in Python - Step 3.mp4 28MB
  162. 16 Logistic Regression/121 Logistic Regression in R - Step 3.mp4 27MB
  163. 14 Regression Model Selection in R/107 Interpreting Linear Regression Coefficients.mp4 27MB
  164. 40 Linear Discriminant Analysis (LDA)/305 Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
  165. 36 Artificial Neural Networks/262 How do Neural Networks learn.mp4 27MB
  166. 10 Decision Tree Regression/092 Decision Tree Regression in Python - Step 2.mp4 26MB
  167. 26 K-Means Clustering/170 K-Means Selecting The Number Of Clusters.mp4 26MB
  168. 22 Random Forest Classification/155 Random Forest Classification Intuition.mp4 26MB
  169. 10 Decision Tree Regression/089 Decision Tree Regression Intuition.mp4 25MB
  170. 30 Eclat/204 Eclat in R.mp4 25MB
  171. 06 Simple Linear Regression/046 Simple Linear Regression in R - Step 2.mp4 25MB
  172. 36 Artificial Neural Networks/261 How do Neural Networks work.mp4 24MB
  173. 07 Multiple Linear Regression/063 Multiple Linear Regression in R - Step 1.mp4 23MB
  174. 01 Welcome to the course/011 Installing R and R Studio (Mac Linux Windows).mp4 23MB
  175. 27 Hierarchical Clustering/180 Hierarchical Clustering Using Dendrograms.mp4 23MB
  176. 34 -------------------- Part 7 Natural Language Processing --------------------/232 Types of Natural Language Processing.mp4 22MB
  177. 07 Multiple Linear Regression/067 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 22MB
  178. 34 -------------------- Part 7 Natural Language Processing --------------------/245 Natural Language Processing in R - Step 2.mp4 22MB
  179. 21 Decision Tree Classification/151 Decision Tree Classification Intuition.mp4 22MB
  180. 12 Evaluating Regression Models Performance/101 Adjusted R-Squared Intuition.mp4 21MB
  181. 08 Polynomial Regression/075 Polynomial Regression in R - Step 1.mp4 21MB
  182. 01 Welcome to the course/008 Updates on Udemy Reviews.mp4 20MB
  183. 24 Evaluating Classification Models Performance/164 CAP Curve.mp4 20MB
  184. 18 Support Vector Machine (SVM)/132 SVM Intuition.mp4 20MB
  185. 09 Support Vector Regression (SVR)/081 Heads-up on non-linear SVR.mp4 20MB
  186. 10 Decision Tree Regression/093 Decision Tree Regression in Python - Step 3.mp4 19MB
  187. 20 Naive Bayes/147 Naive Bayes Intuition (Extras).mp4 19MB
  188. 36 Artificial Neural Networks/263 Gradient Descent.mp4 19MB
  189. 36 Artificial Neural Networks/275 ANN in R - Step 2.mp4 18MB
  190. 32 Upper Confidence Bound (UCB)/210 Upper Confidence Bound in Python - Step 2.mp4 18MB
  191. 16 Logistic Regression/125 R Classification Template.mp4 18MB
  192. 27 Hierarchical Clustering/179 Hierarchical Clustering How Dendrograms Work.mp4 17MB
  193. 34 -------------------- Part 7 Natural Language Processing --------------------/251 Natural Language Processing in R - Step 8.mp4 17MB
  194. 34 -------------------- Part 7 Natural Language Processing --------------------/246 Natural Language Processing in R - Step 3.mp4 17MB
  195. 36 Artificial Neural Networks/264 Stochastic Gradient Descent.mp4 17MB
  196. 07 Multiple Linear Regression/052 Multiple Linear Regression Intuition - Step 3.mp4 17MB
  197. 27 Hierarchical Clustering/178 Hierarchical Clustering Intuition.mp4 17MB
  198. 04 Data Preprocessing in R/030 Importing the Dataset.mp4 16MB
  199. 34 -------------------- Part 7 Natural Language Processing --------------------/249 Natural Language Processing in R - Step 6.mp4 16MB
  200. 03 Data Preprocessing in Python/019 Importing the Libraries.mp4 16MB
  201. 16 Logistic Regression/119 Logistic Regression in R - Step 1.mp4 16MB
  202. 19 Kernel SVM/139 Types of Kernel Functions.mp4 16MB
  203. 11 Random Forest Regression/096 Random Forest Regression Intuition.mp4 16MB
  204. 19 Kernel SVM/137 Mapping to a higher dimension.mp4 15MB
  205. 26 K-Means Clustering/169 K-Means Random Initialization Trap.mp4 15MB
  206. 24 Evaluating Classification Models Performance/161 False Positives False Negatives.mp4 15MB
  207. 16 Logistic Regression/120 Logistic Regression in R - Step 2.mp4 15MB
  208. 36 Artificial Neural Networks/260 The Activation Function.mp4 15MB
  209. 01 Welcome to the course/005 Why Machine Learning is the Future.mp4 14MB
  210. 37 Convolutional Neural Networks/283 Step 1(b) - ReLU Layer.mp4 14MB
  211. 33 Thompson Sampling/221 Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
  212. 27 Hierarchical Clustering/186 Hierarchical Clustering in R - Step 2.mp4 14MB
  213. 07 Multiple Linear Regression/065 Multiple Linear Regression in R - Step 3.mp4 14MB
  214. 27 Hierarchical Clustering/189 Hierarchical Clustering in R - Step 5.mp4 14MB
  215. 20 Naive Bayes/146 Naive Bayes Intuition (Challenge Reveal).mp4 13MB
  216. 24 Evaluating Classification Models Performance/165 CAP Curve Analysis.mp4 13MB
  217. 34 -------------------- Part 7 Natural Language Processing --------------------/231 NLP Intuition.mp4 13MB
  218. 07 Multiple Linear Regression/049 Dataset Business Problem Description.mp4 13MB
  219. 04 Data Preprocessing in R/029 Dataset Description.mp4 12MB
  220. 16 Logistic Regression/122 Logistic Regression in R - Step 4.mp4 12MB
  221. 06 Simple Linear Regression/045 Simple Linear Regression in R - Step 1.mp4 12MB
  222. 06 Simple Linear Regression/047 Simple Linear Regression in R - Step 3.mp4 11MB
  223. 36 Artificial Neural Networks/265 Backpropagation.mp4 11MB
  224. 30 Eclat/201 Eclat Intuition.mp4 11MB
  225. 06 Simple Linear Regression/037 Simple Linear Regression Intuition - Step 1.mp4 11MB
  226. 17 K-Nearest Neighbors (K-NN)/128 K-Nearest Neighbor Intuition.mp4 10MB
  227. 27 Hierarchical Clustering/188 Hierarchical Clustering in R - Step 4.mp4 10MB
  228. 27 Hierarchical Clustering/187 Hierarchical Clustering in R - Step 3.mp4 10MB
  229. 01 Welcome to the course/001 Applications of Machine Learning.mp4 10MB
  230. 04 Data Preprocessing in R/027 Getting Started.mp4 10MB
  231. 12 Evaluating Regression Models Performance/100 R-Squared Intuition.mp4 10MB
  232. 34 -------------------- Part 7 Natural Language Processing --------------------/250 Natural Language Processing in R - Step 7.mp4 10MB
  233. 33 Thompson Sampling/229 Thompson Sampling in R - Step 2.mp4 10MB
  234. 32 Upper Confidence Bound (UCB)/219 Upper Confidence Bound in R - Step 4.mp4 10MB
  235. 08 Polynomial Regression/069 Polynomial Regression Intuition.mp4 9MB
  236. 24 Evaluating Classification Models Performance/162 Confusion Matrix.mp4 9MB
  237. 27 Hierarchical Clustering/185 Hierarchical Clustering in R - Step 1.mp4 9MB
  238. 34 -------------------- Part 7 Natural Language Processing --------------------/247 Natural Language Processing in R - Step 4.mp4 8MB
  239. 37 Convolutional Neural Networks/287 Summary.mp4 8MB
  240. 19 Kernel SVM/136 Kernel SVM Intuition.mp4 6MB
  241. 06 Simple Linear Regression/038 Simple Linear Regression Intuition - Step 2.mp4 6MB
  242. 37 Convolutional Neural Networks/280 Plan of attack.mp4 6MB
  243. 34 -------------------- Part 7 Natural Language Processing --------------------/248 Natural Language Processing in R - Step 5.mp4 6MB
  244. 07 Multiple Linear Regression/053 Multiple Linear Regression Intuition - Step 4.mp4 5MB
  245. 01 Welcome to the course/009 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  246. 03 Data Preprocessing in Python/017 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  247. 06 Simple Linear Regression/039 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  248. 07 Multiple Linear Regression/056 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  249. 08 Polynomial Regression/070 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  250. 09 Support Vector Regression (SVR)/082 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  251. 10 Decision Tree Regression/090 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  252. 11 Random Forest Regression/097 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  253. 16 Logistic Regression/111 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  254. 17 K-Nearest Neighbors (K-NN)/129 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  255. 18 Support Vector Machine (SVM)/133 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  256. 19 Kernel SVM/141 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  257. 20 Naive Bayes/148 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  258. 21 Decision Tree Classification/152 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  259. 22 Random Forest Classification/156 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  260. 26 K-Means Clustering/171 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  261. 27 Hierarchical Clustering/181 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  262. 29 Apriori/193 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  263. 30 Eclat/202 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  264. 32 Upper Confidence Bound (UCB)/208 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  265. 33 Thompson Sampling/222 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  266. 34 -------------------- Part 7 Natural Language Processing --------------------/235 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  267. 36 Artificial Neural Networks/267 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  268. 39 Principal Component Analysis (PCA)/299 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  269. 40 Linear Discriminant Analysis (LDA)/306 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  270. 41 Kernel PCA/309 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  271. 43 Model Selection/313 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  272. 44 XGBoost/318 Machine-Learning-A-Z-Codes-and-Datasets.zip 5MB
  273. 36 Artificial Neural Networks/258 Plan of attack.mp4 5MB
  274. 24 Evaluating Classification Models Performance/163 Accuracy Paradox.mp4 4MB
  275. 37 Convolutional Neural Networks/285 Step 3 - Flattening.mp4 3MB
  276. 01 Welcome to the course/007 Machine-Learning-A-Z-Q-A.pdf 2MB
  277. 07 Multiple Linear Regression/051 Multiple Linear Regression Intuition - Step 2.mp4 2MB
  278. 07 Multiple Linear Regression/050 Multiple Linear Regression Intuition - Step 1.mp4 2MB
  279. 13 Regression Model Selection in Python/105 Regression-Bonus.zip 364KB
  280. 14 Regression Model Selection in R/108 Regression-Bonus.zip 364KB
  281. 13 Regression Model Selection in Python/102 Machine-Learning-A-Z-Model-Selection.zip 160KB
  282. 23 Classification Model Selection in Python/159 Machine-Learning-A-Z-Model-Selection.zip 160KB
  283. 30 Eclat/204 Eclat.zip 49KB
  284. 24 Evaluating Classification Models Performance/166 Classification-Pros-Cons.pdf 29KB
  285. 27 Hierarchical Clustering/190 Clustering-Pros-Cons.pdf 26KB
  286. 18 Support Vector Machine (SVM)/135 SVM.zip 8KB
  287. 45 Bonus Lectures/323 YOUR SPECIAL BONUS.html 6KB
  288. 01 Welcome to the course/015 Your Shortcut To Becoming A Better Data Scientist.html 5KB
  289. 07 Multiple Linear Regression/061 Multiple Linear Regression in Python - Backward Elimination.html 4KB
  290. 24 Evaluating Classification Models Performance/166 Conclusion of Part 3 - Classification.html 4KB
  291. 01 Welcome to the course/006 Important notes tips tricks for this course.html 4KB
  292. 01 Welcome to the course/014 FAQBot.html 4KB
  293. 33 Thompson Sampling/227 Additional Resource for this Section.html 3KB
  294. 01 Welcome to the course/009 GET ALL THE CODES AND DATASETS HERE.html 3KB
  295. 13 Regression Model Selection in Python/105 Conclusion of Part 2 - Regression.html 3KB
  296. 14 Regression Model Selection in R/108 Conclusion of Part 2 - Regression.html 3KB
  297. 34 -------------------- Part 7 Natural Language Processing --------------------/230 Welcome to Part 7 - Natural Language Processing.html 3KB
  298. 31 -------------------- Part 6 Reinforcement Learning --------------------/205 Welcome to Part 6 - Reinforcement Learning.html 2KB
  299. 03 Data Preprocessing in Python/021 For Python learners summary of Object-oriented programming classes objects.html 2KB
  300. 01 Welcome to the course/007 This PDF resource will help you a lot.html 2KB
  301. 34 -------------------- Part 7 Natural Language Processing --------------------/254 Homework Challenge.html 2KB
  302. 01 Welcome to the course/002 BONUS Learning Paths.html 2KB
  303. 34 -------------------- Part 7 Natural Language Processing --------------------/243 Homework Challenge.html 2KB
  304. 16 Logistic Regression/123 Warning - Update.html 2KB
  305. 38 -------------------- Part 9 Dimensionality Reduction --------------------/297 Welcome to Part 9 - Dimensionality Reduction.html 2KB
  306. 07 Multiple Linear Regression/062 Multiple Linear Regression in Python - BONUS.html 2KB
  307. 44 XGBoost/320 Model Selection and Boosting BONUS.html 2KB
  308. 06 Simple Linear Regression/044 Simple Linear Regression in Python - BONUS.html 2KB
  309. 34 -------------------- Part 7 Natural Language Processing --------------------/242 Natural Language Processing in Python - BONUS.html 2KB
  310. 01 Welcome to the course/012 BONUS Meet your instructors.html 2KB
  311. 23 Classification Model Selection in Python/159 Make sure you have this Model Selection folder ready.html 2KB
  312. 36 Artificial Neural Networks/278 Deep Learning BONUS 1.html 2KB
  313. 13 Regression Model Selection in Python/102 Make sure you have this Model Selection folder ready.html 2KB
  314. 42 -------------------- Part 10 Model Selection Boosting --------------------/312 Welcome to Part 10 - Model Selection Boosting.html 2KB
  315. 37 Convolutional Neural Networks/296 Deep Learning BONUS 2.html 2KB
  316. 34 -------------------- Part 7 Natural Language Processing --------------------/255 BONUS NLP BERT.html 2KB
  317. 05 -------------------- Part 2 Regression --------------------/036 Welcome to Part 2 - Regression.html 2KB
  318. 35 -------------------- Part 8 Deep Learning --------------------/256 Welcome to Part 8 - Deep Learning.html 2KB
  319. 16 Logistic Regression/126 Machine Learning Regression and Classification BONUS.html 2KB
  320. 15 -------------------- Part 3 Classification --------------------/109 Welcome to Part 3 - Classification.html 2KB
  321. 37 Convolutional Neural Networks/289 Make sure you have your dataset ready.html 2KB
  322. 06 Simple Linear Regression/039 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  323. 07 Multiple Linear Regression/056 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  324. 08 Polynomial Regression/070 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  325. 09 Support Vector Regression (SVR)/082 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  326. 10 Decision Tree Regression/090 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  327. 11 Random Forest Regression/097 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  328. 16 Logistic Regression/111 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  329. 17 K-Nearest Neighbors (K-NN)/129 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  330. 18 Support Vector Machine (SVM)/133 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  331. 19 Kernel SVM/141 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  332. 20 Naive Bayes/148 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  333. 21 Decision Tree Classification/152 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  334. 22 Random Forest Classification/156 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  335. 26 K-Means Clustering/171 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  336. 27 Hierarchical Clustering/181 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  337. 29 Apriori/193 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  338. 30 Eclat/202 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  339. 32 Upper Confidence Bound (UCB)/208 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  340. 33 Thompson Sampling/222 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  341. 34 -------------------- Part 7 Natural Language Processing --------------------/235 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  342. 36 Artificial Neural Networks/267 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  343. 39 Principal Component Analysis (PCA)/299 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  344. 40 Linear Discriminant Analysis (LDA)/306 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  345. 41 Kernel PCA/309 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  346. 43 Model Selection/313 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  347. 44 XGBoost/318 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  348. 07 Multiple Linear Regression/068 Multiple Linear Regression in R - Automatic Backward Elimination.html 2KB
  349. 25 -------------------- Part 4 Clustering --------------------/167 Welcome to Part 4 - Clustering.html 2KB
  350. 03 Data Preprocessing in Python/017 Make sure you have your Machine Learning A-Z folder ready.html 2KB
  351. 16 Logistic Regression/127 BONUS Logistic Regression Practical Case Study.html 1KB
  352. 04 Data Preprocessing in R/026 Welcome.html 1KB
  353. 01 Welcome to the course/013 Some Additional Resources.html 1KB
  354. 36 Artificial Neural Networks/269 Check out our free course on ANN for Regression.html 1KB
  355. 02 -------------------- Part 1 Data Preprocessing --------------------/016 Welcome to Part 1 - Data Preprocessing.html 1KB
  356. 01 Welcome to the course/003 BONUS 2 ML vs DL vs AI Whats the Difference.html 1KB
  357. 36 Artificial Neural Networks/279 BONUS ANN Case Study.html 1KB
  358. 27 Hierarchical Clustering/190 Conclusion of Part 4 - Clustering.html 1KB
  359. 01 Welcome to the course/004 BONUS 3 Regression Types.html 1KB
  360. 04 Data Preprocessing in R/028 Make sure you have your dataset ready.html 1KB
  361. 28 -------------------- Part 5 Association Rule Learning --------------------/191 Welcome to Part 5 - Association Rule Learning.html 1KB