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

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视频 2019-1-4 16:11 2025-1-4 12:04 281 5.47 GB 257
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FreeCourseSite com Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

文件列表
  1. 01 Welcome to the course!/001 Applications of Machine Learning.mp47.99MB
  2. 01 Welcome to the course!/002 Why Machine Learning is the Future.mp412.81MB
  3. 01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows).mp417.55MB
  4. 01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows).mp419.52MB
  5. 02 -------------------- Part 1_ Data Preprocessing --------------------/007 Welcome to Part 1 - Data Preprocessing.mp42.98MB
  6. 02 -------------------- Part 1_ Data Preprocessing --------------------/008 Get the dataset.mp421.15MB
  7. 02 -------------------- Part 1_ Data Preprocessing --------------------/009 Importing the Libraries.mp411.07MB
  8. 02 -------------------- Part 1_ Data Preprocessing --------------------/010 Importing the Dataset.mp423.31MB
  9. 02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data.mp432.16MB
  10. 02 -------------------- Part 1_ Data Preprocessing --------------------/013 Categorical Data.mp440.79MB
  11. 02 -------------------- Part 1_ Data Preprocessing --------------------/014 Splitting the Dataset into the Training set and Test set.mp439.03MB
  12. 02 -------------------- Part 1_ Data Preprocessing --------------------/015 Feature Scaling.mp434.62MB
  13. 02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!.mp419.67MB
  14. 04 Simple Linear Regression/018 How to get the dataset.mp411.71MB
  15. 04 Simple Linear Regression/019 Dataset + Business Problem Description.mp46.62MB
  16. 04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 1.mp49.47MB
  17. 04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2.mp45.37MB
  18. 04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 1.mp421.72MB
  19. 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2.mp418.75MB
  20. 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3.mp415.61MB
  21. 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 4.mp430.82MB
  22. 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1.mp49.53MB
  23. 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2.mp414.35MB
  24. 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3.mp48.63MB
  25. 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4.mp437.37MB
  26. 05 Multiple Linear Regression/030 How to get the dataset.mp411.71MB
  27. 05 Multiple Linear Regression/031 Dataset + Business Problem Description.mp49.98MB
  28. 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1.mp41.82MB
  29. 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2.mp41.78MB
  30. 05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 3.mp414.28MB
  31. 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 4.mp44.51MB
  32. 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 5.mp428.83MB
  33. 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1.mp439.56MB
  34. 05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 2.mp47.22MB
  35. 05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Step 3.mp414.29MB
  36. 05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp432.82MB
  37. 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp432.59MB
  38. 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp427.17MB
  39. 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1.mp417.94MB
  40. 05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 2.mp425.93MB
  41. 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3.mp410.41MB
  42. 05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp439.73MB
  43. 05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp417.24MB
  44. 06 Polynomial Regression/048 Polynomial Regression Intuition.mp49.44MB
  45. 06 Polynomial Regression/049 How to get the dataset.mp411.71MB
  46. 06 Polynomial Regression/050 Polynomial Regression in Python - Step 1.mp424.89MB
  47. 06 Polynomial Regression/051 Polynomial Regression in Python - Step 2.mp427.1MB
  48. 06 Polynomial Regression/052 Polynomial Regression in Python - Step 3.mp442.98MB
  49. 06 Polynomial Regression/053 Polynomial Regression in Python - Step 4.mp413.5MB
  50. 06 Polynomial Regression/054 Python Regression Template.mp427.43MB
  51. 06 Polynomial Regression/055 Polynomial Regression in R - Step 1.mp417.78MB
  52. 06 Polynomial Regression/056 Polynomial Regression in R - Step 2.mp423.87MB
  53. 06 Polynomial Regression/057 Polynomial Regression in R - Step 3.mp443.31MB
  54. 06 Polynomial Regression/058 Polynomial Regression in R - Step 4.mp422.34MB
  55. 06 Polynomial Regression/059 R Regression Template.mp425.41MB
  56. 07 Support Vector Regression (SVR)/060 How to get the dataset.mp411.71MB
  57. 07 Support Vector Regression (SVR)/061 SVR in Python.mp446.18MB
  58. 07 Support Vector Regression (SVR)/062 SVR in R.mp425.87MB
  59. 08 Decision Tree Regression/063 Decision Tree Regression Intuition.mp422.69MB
  60. 08 Decision Tree Regression/064 How to get the dataset.mp411.71MB
  61. 08 Decision Tree Regression/065 Decision Tree Regression in Python.mp433.54MB
  62. 08 Decision Tree Regression/066 Decision Tree Regression in R.mp444.37MB
  63. 09 Random Forest Regression/067 Random Forest Regression Intuition.mp413.82MB
  64. 09 Random Forest Regression/068 How to get the dataset.mp411.71MB
  65. 09 Random Forest Regression/069 Random Forest Regression in Python.mp439.47MB
  66. 09 Random Forest Regression/070 Random Forest Regression in R.mp440.34MB
  67. 10 Evaluating Regression Models Performance/071 R-Squared Intuition.mp48.85MB
  68. 10 Evaluating Regression Models Performance/072 Adjusted R-Squared Intuition.mp419.28MB
  69. 10 Evaluating Regression Models Performance/073 Evaluating Regression Models Performance - Homework's Final Part.mp421.89MB
  70. 10 Evaluating Regression Models Performance/074 Interpreting Linear Regression Coefficients.mp424.21MB
  71. 12 Logistic Regression/077 Logistic Regression Intuition.mp429.17MB
  72. 12 Logistic Regression/078 How to get the dataset.mp411.71MB
  73. 12 Logistic Regression/079 Logistic Regression in Python - Step 1.mp412.93MB
  74. 12 Logistic Regression/080 Logistic Regression in Python - Step 2.mp48.23MB
  75. 12 Logistic Regression/081 Logistic Regression in Python - Step 3.mp45.97MB
  76. 12 Logistic Regression/082 Logistic Regression in Python - Step 4.mp410.37MB
  77. 12 Logistic Regression/083 Logistic Regression in Python - Step 5.mp442.55MB
  78. 12 Logistic Regression/084 Python Classification Template.mp412.06MB
  79. 12 Logistic Regression/085 Logistic Regression in R - Step 1.mp412.58MB
  80. 12 Logistic Regression/086 Logistic Regression in R - Step 2.mp47.84MB
  81. 12 Logistic Regression/087 Logistic Regression in R - Step 3.mp414.59MB
  82. 12 Logistic Regression/088 Logistic Regression in R - Step 4.mp46.9MB
  83. 12 Logistic Regression/089 Logistic Regression in R - Step 5.mp451.68MB
  84. 12 Logistic Regression/090 R Classification Template.mp412.47MB
  85. 13 K-Nearest Neighbors (K-NN)/091 K-Nearest Neighbor Intuition.mp49.27MB
  86. 13 K-Nearest Neighbors (K-NN)/092 How to get the dataset.mp411.71MB
  87. 13 K-Nearest Neighbors (K-NN)/093 K-NN in Python.mp435.21MB
  88. 13 K-Nearest Neighbors (K-NN)/094 K-NN in R.mp441.37MB
  89. 14 Support Vector Machine (SVM)/095 SVM Intuition.mp418.01MB
  90. 14 Support Vector Machine (SVM)/096 How to get the dataset.mp411.71MB
  91. 14 Support Vector Machine (SVM)/097 SVM in Python.mp431.16MB
  92. 14 Support Vector Machine (SVM)/098 SVM in R.mp432.26MB
  93. 15 Kernel SVM/099 Kernel SVM Intuition.mp45.79MB
  94. 15 Kernel SVM/100 Mapping to a higher dimension.mp413.74MB
  95. 15 Kernel SVM/101 The Kernel Trick.mp429.28MB
  96. 15 Kernel SVM/102 Types of Kernel Functions.mp412.3MB
  97. 15 Kernel SVM/103 How to get the dataset.mp411.71MB
  98. 15 Kernel SVM/104 Kernel SVM in Python.mp441.62MB
  99. 15 Kernel SVM/105 Kernel SVM in R.mp440.45MB
  100. 16 Naive Bayes/106 Bayes Theorem.mp443.9MB
  101. 16 Naive Bayes/107 Naive Bayes Intuition.mp427.79MB
  102. 16 Naive Bayes/108 Naive Bayes Intuition (Challenge Reveal).mp413.27MB
  103. 16 Naive Bayes/109 Naive Bayes Intuition (Extras).mp418.94MB
  104. 16 Naive Bayes/110 How to get the dataset.mp411.71MB
  105. 16 Naive Bayes/111 Naive Bayes in Python.mp423.38MB
  106. 16 Naive Bayes/112 Naive Bayes in R.mp437.31MB
  107. 17 Decision Tree Classification/113 Decision Tree Classification Intuition.mp418.79MB
  108. 17 Decision Tree Classification/114 How to get the dataset.mp411.71MB
  109. 17 Decision Tree Classification/115 Decision Tree Classification in Python.mp429.8MB
  110. 17 Decision Tree Classification/116 Decision Tree Classification in R.mp451.18MB
  111. 18 Random Forest Classification/117 Random Forest Classification Intuition.mp419.43MB
  112. 18 Random Forest Classification/118 How to get the dataset.mp411.71MB
  113. 18 Random Forest Classification/119 Random Forest Classification in Python.mp447.15MB
  114. 18 Random Forest Classification/120 Random Forest Classification in R.mp449.39MB
  115. 19 Evaluating Classification Models Performance/121 False Positives & False Negatives.mp413.65MB
  116. 19 Evaluating Classification Models Performance/122 Confusion Matrix.mp48.21MB
  117. 19 Evaluating Classification Models Performance/123 Accuracy Paradox.mp43.8MB
  118. 19 Evaluating Classification Models Performance/124 CAP Curve.mp418.68MB
  119. 19 Evaluating Classification Models Performance/125 CAP Curve Analysis.mp411.51MB
  120. 21 K-Means Clustering/128 K-Means Clustering Intuition.mp426.86MB
  121. 21 K-Means Clustering/129 K-Means Random Initialization Trap.mp415.36MB
  122. 21 K-Means Clustering/130 K-Means Selecting The Number Of Clusters.mp423.13MB
  123. 21 K-Means Clustering/131 How to get the dataset.mp411.71MB
  124. 21 K-Means Clustering/132 K-Means Clustering in Python.mp439.77MB
  125. 21 K-Means Clustering/133 K-Means Clustering in R.mp428.99MB
  126. 22 Hierarchical Clustering/134 Hierarchical Clustering Intuition.mp416.52MB
  127. 22 Hierarchical Clustering/135 Hierarchical Clustering How Dendrograms Work.mp417.46MB
  128. 22 Hierarchical Clustering/136 Hierarchical Clustering Using Dendrograms.mp422.81MB
  129. 22 Hierarchical Clustering/137 How to get the dataset.mp411.71MB
  130. 22 Hierarchical Clustering/138 HC in Python - Step 1.mp410.72MB
  131. 22 Hierarchical Clustering/139 HC in Python - Step 2.mp412.64MB
  132. 22 Hierarchical Clustering/140 HC in Python - Step 3.mp412.3MB
  133. 22 Hierarchical Clustering/141 HC in Python - Step 4.mp412.02MB
  134. 22 Hierarchical Clustering/142 HC in Python - Step 5.mp48.39MB
  135. 22 Hierarchical Clustering/143 HC in R - Step 1.mp47.38MB
  136. 22 Hierarchical Clustering/144 HC in R - Step 2.mp411.15MB
  137. 22 Hierarchical Clustering/145 HC in R - Step 3.mp47.8MB
  138. 22 Hierarchical Clustering/146 HC in R - Step 4.mp47.44MB
  139. 22 Hierarchical Clustering/147 HC in R - Step 5.mp46.88MB
  140. 24 Apriori/150 Apriori Intuition.mp435.02MB
  141. 24 Apriori/151 How to get the dataset.mp411.71MB
  142. 24 Apriori/152 Apriori in R - Step 1.mp442.87MB
  143. 24 Apriori/153 Apriori in R - Step 2.mp430.5MB
  144. 24 Apriori/154 Apriori in R - Step 3.mp443.84MB
  145. 24 Apriori/155 Apriori in Python - Step 1.mp437.97MB
  146. 24 Apriori/156 Apriori in Python - Step 2.mp429.52MB
  147. 24 Apriori/157 Apriori in Python - Step 3.mp426.96MB
  148. 25 Eclat/158 Eclat Intuition.mp410.65MB
  149. 25 Eclat/159 How to get the dataset.mp411.71MB
  150. 25 Eclat/160 Eclat in R.mp420.68MB
  151. 27 Upper Confidence Bound (UCB)/162 The Multi-Armed Bandit Problem.mp430.19MB
  152. 27 Upper Confidence Bound (UCB)/163 Upper Confidence Bound (UCB) Intuition.mp429.32MB
  153. 27 Upper Confidence Bound (UCB)/164 How to get the dataset.mp411.71MB
  154. 27 Upper Confidence Bound (UCB)/165 Upper Confidence Bound in Python - Step 1.mp431.53MB
  155. 27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2.mp435.44MB
  156. 27 Upper Confidence Bound (UCB)/167 Upper Confidence Bound in Python - Step 3.mp441.11MB
  157. 27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4.mp49.13MB
  158. 27 Upper Confidence Bound (UCB)/169 Upper Confidence Bound in R - Step 1.mp428.05MB
  159. 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound in R - Step 2.mp429.01MB
  160. 27 Upper Confidence Bound (UCB)/171 Upper Confidence Bound in R - Step 3.mp447.2MB
  161. 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in R - Step 4.mp47.4MB
  162. 28 Thompson Sampling/173 Thompson Sampling Intuition.mp437.27MB
  163. 28 Thompson Sampling/174 Algorithm Comparison_ UCB vs Thompson Sampling.mp414.08MB
  164. 28 Thompson Sampling/175 How to get the dataset.mp411.71MB
  165. 28 Thompson Sampling/176 Thompson Sampling in Python - Step 1.mp443.13MB
  166. 28 Thompson Sampling/177 Thompson Sampling in Python - Step 2.mp48.41MB
  167. 28 Thompson Sampling/178 Thompson Sampling in R - Step 1.mp440.93MB
  168. 28 Thompson Sampling/179 Thompson Sampling in R - Step 2.mp47.46MB
  169. 29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset.mp411.71MB
  170. 29 -------------------- Part 7_ Natural Language Processing --------------------/182 Natural Language Processing in Python - Step 1.mp435.2MB
  171. 29 -------------------- Part 7_ Natural Language Processing --------------------/183 Natural Language Processing in Python - Step 2.mp421.96MB
  172. 29 -------------------- Part 7_ Natural Language Processing --------------------/184 Natural Language Processing in Python - Step 3.mp43.38MB
  173. 29 -------------------- Part 7_ Natural Language Processing --------------------/185 Natural Language Processing in Python - Step 4.mp424MB
  174. 29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5.mp414.9MB
  175. 29 -------------------- Part 7_ Natural Language Processing --------------------/187 Natural Language Processing in Python - Step 6.mp46.49MB
  176. 29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7.mp417.1MB
  177. 29 -------------------- Part 7_ Natural Language Processing --------------------/189 Natural Language Processing in Python - Step 8.mp439.48MB
  178. 29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9.mp414.01MB
  179. 29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10.mp424.13MB
  180. 29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1.mp440.37MB
  181. 29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2.mp417.47MB
  182. 29 -------------------- Part 7_ Natural Language Processing --------------------/195 Natural Language Processing in R - Step 3.mp413.52MB
  183. 29 -------------------- Part 7_ Natural Language Processing --------------------/196 Natural Language Processing in R - Step 4.mp46.5MB
  184. 29 -------------------- Part 7_ Natural Language Processing --------------------/197 Natural Language Processing in R - Step 5.mp44.57MB
  185. 29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6.mp412.73MB
  186. 29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7.mp47.51MB
  187. 29 -------------------- Part 7_ Natural Language Processing --------------------/200 Natural Language Processing in R - Step 8.mp413.26MB
  188. 29 -------------------- Part 7_ Natural Language Processing --------------------/201 Natural Language Processing in R - Step 9.mp428.99MB
  189. 29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10.mp441.19MB
  190. 30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_.mp431.31MB
  191. 31 Artificial Neural Networks/206 Plan of attack.mp44.74MB
  192. 31 Artificial Neural Networks/207 The Neuron.mp429.86MB
  193. 31 Artificial Neural Networks/208 The Activation Function.mp414.75MB
  194. 31 Artificial Neural Networks/209 How do Neural Networks work_.mp423.53MB
  195. 31 Artificial Neural Networks/210 How do Neural Networks learn_.mp426.55MB
  196. 31 Artificial Neural Networks/211 Gradient Descent.mp418.53MB
  197. 31 Artificial Neural Networks/212 Stochastic Gradient Descent.mp416.82MB
  198. 31 Artificial Neural Networks/213 Backpropagation.mp410.92MB
  199. 31 Artificial Neural Networks/214 How to get the dataset.mp411.71MB
  200. 31 Artificial Neural Networks/215 Business Problem Description.mp416.37MB
  201. 31 Artificial Neural Networks/216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras.mp429.31MB
  202. 31 Artificial Neural Networks/217 ANN in Python - Step 2.mp448.09MB
  203. 31 Artificial Neural Networks/218 ANN in Python - Step 3.mp48.37MB
  204. 31 Artificial Neural Networks/219 ANN in Python - Step 4.mp45.88MB
  205. 31 Artificial Neural Networks/220 ANN in Python - Step 5.mp429.58MB
  206. 31 Artificial Neural Networks/221 ANN in Python - Step 6.mp47.05MB
  207. 31 Artificial Neural Networks/222 ANN in Python - Step 7.mp48.99MB
  208. 31 Artificial Neural Networks/223 ANN in Python - Step 8.mp418.16MB
  209. 31 Artificial Neural Networks/224 ANN in Python - Step 9.mp416.89MB
  210. 31 Artificial Neural Networks/225 ANN in Python - Step 10.mp417.09MB
  211. 31 Artificial Neural Networks/226 ANN in R - Step 1.mp438.55MB
  212. 31 Artificial Neural Networks/227 ANN in R - Step 2.mp414.17MB
  213. 31 Artificial Neural Networks/228 ANN in R - Step 3.mp428.94MB
  214. 31 Artificial Neural Networks/229 ANN in R - Step 4 (Last step).mp433.44MB
  215. 32 Convolutional Neural Networks/230 Plan of attack.mp45.9MB
  216. 32 Convolutional Neural Networks/231 What are convolutional neural networks_.mp429.5MB
  217. 32 Convolutional Neural Networks/232 Step 1 - Convolution Operation.mp431.02MB
  218. 32 Convolutional Neural Networks/233 Step 1(b) - ReLU Layer.mp414.09MB
  219. 32 Convolutional Neural Networks/234 Step 2 - Pooling.mp440.24MB
  220. 32 Convolutional Neural Networks/235 Step 3 - Flattening.mp43.27MB
  221. 32 Convolutional Neural Networks/236 Step 4 - Full Connection.mp442.74MB
  222. 32 Convolutional Neural Networks/237 Summary.mp47.91MB
  223. 32 Convolutional Neural Networks/238 Softmax & Cross-Entropy.mp433.23MB
  224. 32 Convolutional Neural Networks/239 How to get the dataset.mp411.71MB
  225. 32 Convolutional Neural Networks/240 CNN in Python - Step 1.mp424.92MB
  226. 32 Convolutional Neural Networks/241 CNN in Python - Step 2.mp45.85MB
  227. 32 Convolutional Neural Networks/242 CNN in Python - Step 3.mp42.22MB
  228. 32 Convolutional Neural Networks/243 CNN in Python - Step 4.mp427.18MB
  229. 32 Convolutional Neural Networks/244 CNN in Python - Step 5.mp49.91MB
  230. 32 Convolutional Neural Networks/245 CNN in Python - Step 6.mp49.7MB
  231. 32 Convolutional Neural Networks/246 CNN in Python - Step 7.mp412.93MB
  232. 32 Convolutional Neural Networks/247 CNN in Python - Step 8.mp46.79MB
  233. 32 Convolutional Neural Networks/248 CNN in Python - Step 9.mp446.85MB
  234. 32 Convolutional Neural Networks/249 CNN in Python - Step 10.mp420.6MB
  235. 34 Principal Component Analysis (PCA)/252 How to get the dataset.mp411.71MB
  236. 34 Principal Component Analysis (PCA)/253 PCA in Python - Step 1.mp431.95MB
  237. 34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2.mp422.07MB
  238. 34 Principal Component Analysis (PCA)/255 PCA in Python - Step 3.mp425.51MB
  239. 34 Principal Component Analysis (PCA)/256 PCA in R - Step 1.mp430.65MB
  240. 34 Principal Component Analysis (PCA)/257 PCA in R - Step 2.mp429.02MB
  241. 34 Principal Component Analysis (PCA)/258 PCA in R - Step 3.mp436.73MB
  242. 35 Linear Discriminant Analysis (LDA)/259 How to get the dataset.mp411.71MB
  243. 35 Linear Discriminant Analysis (LDA)/260 LDA in Python.mp445.42MB
  244. 35 Linear Discriminant Analysis (LDA)/261 LDA in R.mp451.29MB
  245. 36 Kernel PCA/262 How to get the dataset.mp411.71MB
  246. 36 Kernel PCA/263 Kernel PCA in Python.mp433.38MB
  247. 36 Kernel PCA/264 Kernel PCA in R.mp456.57MB
  248. 38 Model Selection/266 How to get the dataset.mp411.71MB
  249. 38 Model Selection/267 k-Fold Cross Validation in Python.mp432.83MB
  250. 38 Model Selection/268 k-Fold Cross Validation in R.mp443.63MB
  251. 38 Model Selection/269 Grid Search in Python - Step 1.mp438.21MB
  252. 38 Model Selection/270 Grid Search in Python - Step 2.mp429.51MB
  253. 38 Model Selection/271 Grid Search in R.mp435.54MB
  254. 39 XGBoost/272 How to get the dataset.mp411.71MB
  255. 39 XGBoost/273 XGBoost in Python - Step 1.mp421.39MB
  256. 39 XGBoost/274 XGBoost in Python - Step 2.mp431.97MB
  257. 39 XGBoost/275 XGBoost in R.mp447.26MB
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