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

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视频 2019-6-9 09:24 2024-11-5 00:38 222 6.82 GB 261
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UdemyCourseDownloader Machine Learning A-Z™ Hands-On Python & R In Data Science

文件列表
  1. 12 Logistic Regression/096 Logistic Regression in R - Step 5.mp493.76MB
  2. 01 Welcome to the course/001 Applications of Machine Learning.mp49.81MB
  3. 01 Welcome to the course/002 Why Machine Learning is the Future.mp414.48MB
  4. 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp423.96MB
  5. 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp423.21MB
  6. 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing.mp43.52MB
  7. 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp421.15MB
  8. 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries.mp413.56MB
  9. 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp428.64MB
  10. 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp432.16MB
  11. 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp452.88MB
  12. 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set.mp450.91MB
  13. 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp444.59MB
  14. 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp425.86MB
  15. 04 Simple Linear Regression/021 How to get the dataset.mp411.71MB
  16. 04 Simple Linear Regression/022 Dataset Business Problem Description.mp47.77MB
  17. 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1.mp410.52MB
  18. 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2.mp45.99MB
  19. 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1.mp427.92MB
  20. 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2.mp424.62MB
  21. 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3.mp420.55MB
  22. 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4.mp439.37MB
  23. 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1.mp411.52MB
  24. 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2.mp424.87MB
  25. 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3.mp411.42MB
  26. 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4.mp449.16MB
  27. 05 Multiple Linear Regression/033 How to get the dataset.mp411.71MB
  28. 05 Multiple Linear Regression/034 Dataset Business Problem Description.mp412.56MB
  29. 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1.mp42MB
  30. 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2.mp42.03MB
  31. 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3.mp416.59MB
  32. 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4.mp45.34MB
  33. 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5.mp432.8MB
  34. 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1.mp452.18MB
  35. 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2.mp49.84MB
  36. 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3.mp425.48MB
  37. 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp423.82MB
  38. 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp459.14MB
  39. 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp454.26MB
  40. 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1.mp423.44MB
  41. 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2.mp445.22MB
  42. 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3.mp413.85MB
  43. 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp450.78MB
  44. 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp421.95MB
  45. 06 Polynomial Regression/054 Polynomial Regression Intuition.mp49.44MB
  46. 06 Polynomial Regression/055 How to get the dataset.mp411.71MB
  47. 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1.mp431.64MB
  48. 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2.mp435.11MB
  49. 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3.mp454.5MB
  50. 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4.mp417.65MB
  51. 06 Polynomial Regression/060 Python Regression Template.mp436.78MB
  52. 06 Polynomial Regression/061 Polynomial Regression in R - Step 1.mp421.21MB
  53. 06 Polynomial Regression/062 Polynomial Regression in R - Step 2.mp432.28MB
  54. 06 Polynomial Regression/063 Polynomial Regression in R - Step 3.mp454.8MB
  55. 06 Polynomial Regression/064 Polynomial Regression in R - Step 4.mp428.52MB
  56. 06 Polynomial Regression/065 R Regression Template.mp431.33MB
  57. 07 Support Vector Regression (SVR)/066 How to get the dataset.mp411.71MB
  58. 07 Support Vector Regression (SVR)/067 SVR Intuition.mp446.59MB
  59. 07 Support Vector Regression (SVR)/068 SVR in Python.mp460.22MB
  60. 07 Support Vector Regression (SVR)/069 SVR in R.mp433.73MB
  61. 08 Decision Tree Regression/070 Decision Tree Regression Intuition.mp425.33MB
  62. 08 Decision Tree Regression/071 How to get the dataset.mp411.71MB
  63. 08 Decision Tree Regression/072 Decision Tree Regression in Python.mp443.44MB
  64. 08 Decision Tree Regression/073 Decision Tree Regression in R.mp456.23MB
  65. 09 Random Forest Regression/074 Random Forest Regression Intuition.mp415.65MB
  66. 09 Random Forest Regression/075 How to get the dataset.mp411.71MB
  67. 09 Random Forest Regression/076 Random Forest Regression in Python.mp452.69MB
  68. 09 Random Forest Regression/077 Random Forest Regression in R.mp451.86MB
  69. 10 Evaluating Regression Models Performance/078 R-Squared Intuition.mp49.8MB
  70. 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition.mp421.41MB
  71. 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part.mp428.35MB
  72. 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients.mp427.38MB
  73. 12 Logistic Regression/084 Logistic Regression Intuition.mp429.17MB
  74. 12 Logistic Regression/085 How to get the dataset.mp411.71MB
  75. 12 Logistic Regression/086 Logistic Regression in Python - Step 1.mp416.84MB
  76. 12 Logistic Regression/087 Logistic Regression in Python - Step 2.mp411.1MB
  77. 12 Logistic Regression/088 Logistic Regression in Python - Step 3.mp47.98MB
  78. 12 Logistic Regression/089 Logistic Regression in Python - Step 4.mp413.87MB
  79. 12 Logistic Regression/090 Logistic Regression in Python - Step 5.mp453.15MB
  80. 12 Logistic Regression/091 Python Classification Template.mp417.58MB
  81. 12 Logistic Regression/092 Logistic Regression in R - Step 1.mp415.72MB
  82. 12 Logistic Regression/093 Logistic Regression in R - Step 2.mp414.85MB
  83. 12 Logistic Regression/094 Logistic Regression in R - Step 3.mp427.44MB
  84. 12 Logistic Regression/095 Logistic Regression in R - Step 4.mp411.73MB
  85. 12 Logistic Regression/097 R Classification Template.mp417.5MB
  86. 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition.mp410.48MB
  87. 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset.mp411.71MB
  88. 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python.mp446.98MB
  89. 13 K-Nearest Neighbors (K-NN)/101 K-NN in R.mp455.77MB
  90. 14 Support Vector Machine (SVM)/102 SVM Intuition.mp419.92MB
  91. 14 Support Vector Machine (SVM)/103 How to get the dataset.mp411.71MB
  92. 14 Support Vector Machine (SVM)/104 SVM in Python.mp441.71MB
  93. 14 Support Vector Machine (SVM)/105 SVM in R.mp465.31MB
  94. 15 Kernel SVM/106 Kernel SVM Intuition.mp46.42MB
  95. 15 Kernel SVM/107 Mapping to a higher dimension.mp415.39MB
  96. 15 Kernel SVM/108 The Kernel Trick.mp434.72MB
  97. 15 Kernel SVM/109 Types of Kernel Functions.mp415.71MB
  98. 15 Kernel SVM/110 How to get the dataset.mp411.71MB
  99. 15 Kernel SVM/111 Kernel SVM in Python.mp454.86MB
  100. 15 Kernel SVM/112 Kernel SVM in R.mp452.82MB
  101. 16 Naive Bayes/113 Bayes Theorem.mp450.43MB
  102. 16 Naive Bayes/114 Naive Bayes Intuition.mp431.1MB
  103. 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal).mp413.27MB
  104. 16 Naive Bayes/116 Naive Bayes Intuition (Extras).mp418.94MB
  105. 16 Naive Bayes/117 How to get the dataset.mp411.71MB
  106. 16 Naive Bayes/118 Naive Bayes in Python.mp431.14MB
  107. 16 Naive Bayes/119 Naive Bayes in R.mp449.79MB
  108. 17 Decision Tree Classification/120 Decision Tree Classification Intuition.mp421.63MB
  109. 17 Decision Tree Classification/121 How to get the dataset.mp411.71MB
  110. 17 Decision Tree Classification/122 Decision Tree Classification in Python.mp438.85MB
  111. 17 Decision Tree Classification/123 Decision Tree Classification in R.mp468.18MB
  112. 18 Random Forest Classification/124 Random Forest Classification Intuition.mp425.66MB
  113. 18 Random Forest Classification/125 How to get the dataset.mp411.71MB
  114. 18 Random Forest Classification/126 Random Forest Classification in Python.mp462.04MB
  115. 18 Random Forest Classification/127 Random Forest Classification in R.mp464.11MB
  116. 19 Evaluating Classification Models Performance/128 False Positives False Negatives.mp415.12MB
  117. 19 Evaluating Classification Models Performance/129 Confusion Matrix.mp48.91MB
  118. 19 Evaluating Classification Models Performance/130 Accuracy Paradox.mp44.21MB
  119. 19 Evaluating Classification Models Performance/131 CAP Curve.mp420.31MB
  120. 19 Evaluating Classification Models Performance/132 CAP Curve Analysis.mp412.94MB
  121. 21 K-Means Clustering/135 K-Means Clustering Intuition.mp429.97MB
  122. 21 K-Means Clustering/136 K-Means Random Initialization Trap.mp415.36MB
  123. 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters.mp425.68MB
  124. 21 K-Means Clustering/138 How to get the dataset.mp411.71MB
  125. 21 K-Means Clustering/139 K-Means Clustering in Python.mp449.81MB
  126. 21 K-Means Clustering/140 K-Means Clustering in R.mp436.91MB
  127. 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition.mp416.52MB
  128. 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work.mp417.46MB
  129. 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms.mp422.81MB
  130. 22 Hierarchical Clustering/144 How to get the dataset.mp411.71MB
  131. 22 Hierarchical Clustering/145 HC in Python - Step 1.mp413.77MB
  132. 22 Hierarchical Clustering/146 HC in Python - Step 2.mp415.51MB
  133. 22 Hierarchical Clustering/147 HC in Python - Step 3.mp416.17MB
  134. 22 Hierarchical Clustering/148 HC in Python - Step 4.mp421.32MB
  135. 22 Hierarchical Clustering/149 HC in Python - Step 5.mp49.92MB
  136. 22 Hierarchical Clustering/150 HC in R - Step 1.mp48.59MB
  137. 22 Hierarchical Clustering/151 HC in R - Step 2.mp413.87MB
  138. 22 Hierarchical Clustering/152 HC in R - Step 3.mp49.95MB
  139. 22 Hierarchical Clustering/153 HC in R - Step 4.mp410.17MB
  140. 22 Hierarchical Clustering/154 HC in R - Step 5.mp413.68MB
  141. 24 Apriori/157 Apriori Intuition.mp435.02MB
  142. 24 Apriori/158 How to get the dataset.mp411.71MB
  143. 24 Apriori/159 Apriori in R - Step 1.mp452.83MB
  144. 24 Apriori/160 Apriori in R - Step 2.mp438.81MB
  145. 24 Apriori/161 Apriori in R - Step 3.mp456.51MB
  146. 24 Apriori/162 Apriori in Python - Step 1.mp447.41MB
  147. 24 Apriori/163 Apriori in Python - Step 2.mp437.32MB
  148. 24 Apriori/164 Apriori in Python - Step 3.mp435.3MB
  149. 25 Eclat/165 Eclat Intuition.mp410.65MB
  150. 25 Eclat/166 How to get the dataset.mp411.71MB
  151. 25 Eclat/167 Eclat in R.mp425.26MB
  152. 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem.mp430.19MB
  153. 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition.mp429.32MB
  154. 27 Upper Confidence Bound (UCB)/171 How to get the dataset.mp411.71MB
  155. 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1.mp439.01MB
  156. 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2.mp444.49MB
  157. 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3.mp453.71MB
  158. 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4.mp412.44MB
  159. 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1.mp434.01MB
  160. 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2.mp434.1MB
  161. 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3.mp457.84MB
  162. 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4.mp49.55MB
  163. 28 Thompson Sampling/180 Thompson Sampling Intuition.mp437.27MB
  164. 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling.mp414.08MB
  165. 28 Thompson Sampling/182 How to get the dataset.mp411.71MB
  166. 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1.mp455.52MB
  167. 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2.mp411.22MB
  168. 28 Thompson Sampling/185 Thompson Sampling in R - Step 1.mp451.04MB
  169. 28 Thompson Sampling/186 Thompson Sampling in R - Step 2.mp49.56MB
  170. 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition.mp429.69MB
  171. 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset.mp411.71MB
  172. 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1.mp446.06MB
  173. 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2.mp427.44MB
  174. 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3.mp44.16MB
  175. 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4.mp429.75MB
  176. 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5.mp418.8MB
  177. 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6.mp48.32MB
  178. 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7.mp422.13MB
  179. 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8.mp452.02MB
  180. 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9.mp418.9MB
  181. 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10.mp432.91MB
  182. 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1.mp451.2MB
  183. 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2.mp421.66MB
  184. 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3.mp416.89MB
  185. 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4.mp48.24MB
  186. 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5.mp45.78MB
  187. 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6.mp416.09MB
  188. 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7.mp49.59MB
  189. 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8.mp417.23MB
  190. 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9.mp437.69MB
  191. 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10.mp454.14MB
  192. 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning.mp431.31MB
  193. 31 Artificial Neural Networks/214 Plan of attack.mp44.74MB
  194. 31 Artificial Neural Networks/215 The Neuron.mp429.86MB
  195. 31 Artificial Neural Networks/216 The Activation Function.mp414.75MB
  196. 31 Artificial Neural Networks/217 How do Neural Networks work.mp423.53MB
  197. 31 Artificial Neural Networks/218 How do Neural Networks learn.mp426.55MB
  198. 31 Artificial Neural Networks/219 Gradient Descent.mp418.53MB
  199. 31 Artificial Neural Networks/220 Stochastic Gradient Descent.mp416.82MB
  200. 31 Artificial Neural Networks/221 Backpropagation.mp410.92MB
  201. 31 Artificial Neural Networks/222 How to get the dataset.mp411.71MB
  202. 31 Artificial Neural Networks/223 Business Problem Description.mp429.23MB
  203. 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp437.45MB
  204. 31 Artificial Neural Networks/225 ANN in Python - Step 2.mp484.87MB
  205. 31 Artificial Neural Networks/226 ANN in Python - Step 3.mp414.62MB
  206. 31 Artificial Neural Networks/227 ANN in Python - Step 4.mp49.69MB
  207. 31 Artificial Neural Networks/228 ANN in Python - Step 5.mp439.36MB
  208. 31 Artificial Neural Networks/229 ANN in Python - Step 6.mp411.93MB
  209. 31 Artificial Neural Networks/230 ANN in Python - Step 7.mp414.92MB
  210. 31 Artificial Neural Networks/231 ANN in Python - Step 8.mp434.03MB
  211. 31 Artificial Neural Networks/232 ANN in Python - Step 9.mp428.47MB
  212. 31 Artificial Neural Networks/233 ANN in Python - Step 10.mp428.42MB
  213. 31 Artificial Neural Networks/234 ANN in R - Step 1.mp449.89MB
  214. 31 Artificial Neural Networks/235 ANN in R - Step 2.mp418.24MB
  215. 31 Artificial Neural Networks/236 ANN in R - Step 3.mp437.85MB
  216. 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step).mp443.75MB
  217. 32 Convolutional Neural Networks/238 Plan of attack.mp45.9MB
  218. 32 Convolutional Neural Networks/239 What are convolutional neural networks.mp429.5MB
  219. 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation.mp431.02MB
  220. 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer.mp414.09MB
  221. 32 Convolutional Neural Networks/242 Step 2 - Pooling.mp440.24MB
  222. 32 Convolutional Neural Networks/243 Step 3 - Flattening.mp43.27MB
  223. 32 Convolutional Neural Networks/244 Step 4 - Full Connection.mp442.74MB
  224. 32 Convolutional Neural Networks/245 Summary.mp47.91MB
  225. 32 Convolutional Neural Networks/246 Softmax Cross-Entropy.mp433.23MB
  226. 32 Convolutional Neural Networks/247 How to get the dataset.mp411.71MB
  227. 32 Convolutional Neural Networks/248 CNN in Python - Step 1.mp430.6MB
  228. 32 Convolutional Neural Networks/249 CNN in Python - Step 2.mp47.2MB
  229. 32 Convolutional Neural Networks/250 CNN in Python - Step 3.mp42.8MB
  230. 32 Convolutional Neural Networks/251 CNN in Python - Step 4.mp434.62MB
  231. 32 Convolutional Neural Networks/252 CNN in Python - Step 5.mp412.38MB
  232. 32 Convolutional Neural Networks/253 CNN in Python - Step 6.mp411.94MB
  233. 32 Convolutional Neural Networks/254 CNN in Python - Step 7.mp416.65MB
  234. 32 Convolutional Neural Networks/255 CNN in Python - Step 8.mp48.95MB
  235. 32 Convolutional Neural Networks/256 CNN in Python - Step 9.mp462.41MB
  236. 32 Convolutional Neural Networks/257 CNN in Python - Step 10.mp427.74MB
  237. 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition.mp432.11MB
  238. 34 Principal Component Analysis (PCA)/261 How to get the dataset.mp411.71MB
  239. 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1.mp431.95MB
  240. 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2.mp422.07MB
  241. 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3.mp425.51MB
  242. 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1.mp430.65MB
  243. 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2.mp429.02MB
  244. 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3.mp436.73MB
  245. 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition.mp426.98MB
  246. 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset.mp411.71MB
  247. 35 Linear Discriminant Analysis (LDA)/270 LDA in Python.mp445.42MB
  248. 35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp451.29MB
  249. 36 Kernel PCA/272 How to get the dataset.mp411.71MB
  250. 36 Kernel PCA/273 Kernel PCA in Python.mp433.38MB
  251. 36 Kernel PCA/274 Kernel PCA in R.mp456.57MB
  252. 38 Model Selection/276 How to get the dataset.mp411.71MB
  253. 38 Model Selection/277 k-Fold Cross Validation in Python.mp432.83MB
  254. 38 Model Selection/278 k-Fold Cross Validation in R.mp443.63MB
  255. 38 Model Selection/279 Grid Search in Python - Step 1.mp438.21MB
  256. 38 Model Selection/280 Grid Search in Python - Step 2.mp429.51MB
  257. 38 Model Selection/281 Grid Search in R.mp435.54MB
  258. 39 XGBoost/282 How to get the dataset.mp411.71MB
  259. 39 XGBoost/283 XGBoost in Python - Step 1.mp421.39MB
  260. 39 XGBoost/284 XGBoost in Python - Step 2.mp431.97MB
  261. 39 XGBoost/285 XGBoost in R.mp447.26MB
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