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

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DesireCourse Net Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

文件列表
  1. 1. Welcome to the course!/1. Applications of Machine Learning.mp49.81MB
  2. 1. Welcome to the course!/10. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp494.79MB
  3. 1. Welcome to the course!/11. Installing R and R Studio (Mac, Linux & Windows).mp423.22MB
  4. 1. Welcome to the course!/5. Why Machine Learning is the Future.mp414.49MB
  5. 1. Welcome to the course!/8. Updates on Udemy Reviews.mp420.42MB
  6. 10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp425.34MB
  7. 10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp442.4MB
  8. 10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp426.26MB
  9. 10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp419.47MB
  10. 10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp454.79MB
  11. 10. Decision Tree Regression/7. Decision Tree Regression in R.mp456.24MB
  12. 11. Random Forest Regression/1. Random Forest Regression Intuition.mp415.66MB
  13. 11. Random Forest Regression/3. Random Forest Regression in Python.mp474.4MB
  14. 11. Random Forest Regression/4. Random Forest Regression in R.mp451.87MB
  15. 12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp49.8MB
  16. 12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp421.41MB
  17. 13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4123.59MB
  18. 13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp456.78MB
  19. 14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp428.35MB
  20. 14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp427.39MB
  21. 16. Logistic Regression/1. Logistic Regression Intuition.mp429.18MB
  22. 16. Logistic Regression/10. Logistic Regression in R - Step 1.mp415.73MB
  23. 16. Logistic Regression/11. Logistic Regression in R - Step 2.mp414.85MB
  24. 16. Logistic Regression/12. Logistic Regression in R - Step 3.mp427.45MB
  25. 16. Logistic Regression/13. Logistic Regression in R - Step 4.mp411.74MB
  26. 16. Logistic Regression/15. Logistic Regression in R - Step 5.mp493.77MB
  27. 16. Logistic Regression/16. R Classification Template.mp417.51MB
  28. 16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp444.6MB
  29. 16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp484.67MB
  30. 16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp443.06MB
  31. 16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp445.19MB
  32. 16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp430.59MB
  33. 16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp452.96MB
  34. 16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4118.63MB
  35. 17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp410.49MB
  36. 17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4146.62MB
  37. 17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp455.78MB
  38. 18. Support Vector Machine (SVM)/2. SVM Intuition.mp419.93MB
  39. 18. Support Vector Machine (SVM)/4. SVM in Python.mp4104.76MB
  40. 18. Support Vector Machine (SVM)/5. SVM in R.mp465.32MB
  41. 19. Kernel SVM/1. Kernel SVM Intuition.mp46.43MB
  42. 19. Kernel SVM/2. Mapping to a higher dimension.mp415.39MB
  43. 19. Kernel SVM/3. The Kernel Trick.mp434.73MB
  44. 19. Kernel SVM/4. Types of Kernel Functions.mp415.72MB
  45. 19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp465.65MB
  46. 19. Kernel SVM/7. Kernel SVM in Python.mp488.36MB
  47. 19. Kernel SVM/8. Kernel SVM in R.mp452.82MB
  48. 20. Naive Bayes/1. Bayes Theorem.mp450.44MB
  49. 20. Naive Bayes/2. Naive Bayes Intuition.mp431.1MB
  50. 20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp413.28MB
  51. 20. Naive Bayes/4. Naive Bayes Intuition (Extras).mp418.95MB
  52. 20. Naive Bayes/6. Naive Bayes in Python.mp4100.46MB
  53. 20. Naive Bayes/7. Naive Bayes in R.mp449.8MB
  54. 21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp421.63MB
  55. 21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4108.06MB
  56. 21. Decision Tree Classification/4. Decision Tree Classification in R.mp468.19MB
  57. 22. Random Forest Classification/1. Random Forest Classification Intuition.mp425.67MB
  58. 22. Random Forest Classification/3. Random Forest Classification in Python.mp496.7MB
  59. 22. Random Forest Classification/4. Random Forest Classification in R.mp464.12MB
  60. 23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4136MB
  61. 24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp415.13MB
  62. 24. Evaluating Classification Models Performance/2. Confusion Matrix.mp48.92MB
  63. 24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp44.22MB
  64. 24. Evaluating Classification Models Performance/4. CAP Curve.mp420.32MB
  65. 24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp412.94MB
  66. 26. K-Means Clustering/1. K-Means Clustering Intuition.mp429.98MB
  67. 26. K-Means Clustering/10. K-Means Clustering in R.mp436.91MB
  68. 26. K-Means Clustering/2. K-Means Random Initialization Trap.mp415.37MB
  69. 26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp425.69MB
  70. 26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp438.1MB
  71. 26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp454.08MB
  72. 26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp481.34MB
  73. 26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp435.11MB
  74. 26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4120.51MB
  75. 27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp413.87MB
  76. 27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp49.96MB
  77. 27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp410.17MB
  78. 27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp413.69MB
  79. 27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp416.53MB
  80. 27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp417.47MB
  81. 27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp422.82MB
  82. 27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp440.23MB
  83. 27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4135.93MB
  84. 27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp475.29MB
  85. 27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp48.59MB
  86. 29. Apriori/1. Apriori Intuition.mp435.03MB
  87. 29. Apriori/3. Apriori in Python - Step 1.mp469.85MB
  88. 29. Apriori/4. Apriori in Python - Step 2.mp4107.7MB
  89. 29. Apriori/5. Apriori in Python - Step 3.mp469.2MB
  90. 29. Apriori/6. Apriori in Python - Step 4.mp4164.33MB
  91. 29. Apriori/7. Apriori in R - Step 1.mp452.84MB
  92. 29. Apriori/8. Apriori in R - Step 2.mp438.82MB
  93. 29. Apriori/9. Apriori in R - Step 3.mp456.51MB
  94. 3. Data Preprocessing in Python/2. Getting Started.mp454.34MB
  95. 3. Data Preprocessing in Python/3. Importing the Libraries.mp415.98MB
  96. 3. Data Preprocessing in Python/4. Importing the Dataset.mp471.79MB
  97. 3. Data Preprocessing in Python/6. Taking care of Missing Data.mp469.02MB
  98. 3. Data Preprocessing in Python/7. Encoding Categorical Data.mp488.64MB
  99. 3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp467.63MB
  100. 3. Data Preprocessing in Python/9. Feature Scaling.mp4101.72MB
  101. 30. Eclat/1. Eclat Intuition.mp410.66MB
  102. 30. Eclat/3. Eclat in Python.mp475.55MB
  103. 30. Eclat/4. Eclat in R.mp425.27MB
  104. 32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp430.2MB
  105. 32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp443.34MB
  106. 32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp434.02MB
  107. 32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp434.11MB
  108. 32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp457.85MB
  109. 32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp49.56MB
  110. 32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp429.33MB
  111. 32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp458.75MB
  112. 32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp417.75MB
  113. 32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp438.47MB
  114. 32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp485.39MB
  115. 32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp432.44MB
  116. 32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp444.9MB
  117. 33. Thompson Sampling/1. Thompson Sampling Intuition.mp437.28MB
  118. 33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp49.57MB
  119. 33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp414.09MB
  120. 33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp430.59MB
  121. 33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp470.02MB
  122. 33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp478.66MB
  123. 33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp444.65MB
  124. 33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp451.05MB
  125. 34. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 4.mp460.1MB
  126. 34. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 5.mp489.62MB
  127. 34. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 6.mp452.91MB
  128. 34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp451.21MB
  129. 34. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp421.66MB
  130. 34. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp416.9MB
  131. 34. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp48.25MB
  132. 34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp45.79MB
  133. 34. -------------------- Part 7 Natural Language Processing --------------------/2. NLP Intuition.mp412.72MB
  134. 34. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp416.09MB
  135. 34. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp49.6MB
  136. 34. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp417.24MB
  137. 34. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp437.7MB
  138. 34. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp454.15MB
  139. 34. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.mp422.51MB
  140. 34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.mp483.96MB
  141. 34. -------------------- Part 7 Natural Language Processing --------------------/5. Bag-Of-Words Model.mp4103.5MB
  142. 34. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 1.mp434.06MB
  143. 34. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 2.mp440.48MB
  144. 34. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.mp460.61MB
  145. 35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp431.31MB
  146. 36. Artificial Neural Networks/1. Plan of attack.mp44.75MB
  147. 36. Artificial Neural Networks/11. ANN in Python - Step 1.mp466.48MB
  148. 36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4111.03MB
  149. 36. Artificial Neural Networks/14. ANN in Python - Step 3.mp475.08MB
  150. 36. Artificial Neural Networks/15. ANN in Python - Step 4.mp465.37MB
  151. 36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4101.35MB
  152. 36. Artificial Neural Networks/17. ANN in R - Step 1.mp449.9MB
  153. 36. Artificial Neural Networks/18. ANN in R - Step 2.mp418.25MB
  154. 36. Artificial Neural Networks/19. ANN in R - Step 3.mp437.86MB
  155. 36. Artificial Neural Networks/2. The Neuron.mp429.87MB
  156. 36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp443.75MB
  157. 36. Artificial Neural Networks/3. The Activation Function.mp414.75MB
  158. 36. Artificial Neural Networks/4. How do Neural Networks work.mp423.54MB
  159. 36. Artificial Neural Networks/5. How do Neural Networks learn.mp426.56MB
  160. 36. Artificial Neural Networks/6. Gradient Descent.mp418.54MB
  161. 36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp416.83MB
  162. 36. Artificial Neural Networks/8. Backpropagation.mp410.93MB
  163. 36. Artificial Neural Networks/9. Business Problem Description.mp429.24MB
  164. 37. Convolutional Neural Networks/1. Plan of attack.mp45.91MB
  165. 37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp470.8MB
  166. 37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4106.88MB
  167. 37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4118.58MB
  168. 37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp440.02MB
  169. 37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp497.68MB
  170. 37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4152.78MB
  171. 37. Convolutional Neural Networks/2. What are convolutional neural networks.mp429.51MB
  172. 37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp431.03MB
  173. 37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp414.1MB
  174. 37. Convolutional Neural Networks/5. Step 2 - Pooling.mp440.25MB
  175. 37. Convolutional Neural Networks/6. Step 3 - Flattening.mp43.28MB
  176. 37. Convolutional Neural Networks/7. Step 4 - Full Connection.mp442.75MB
  177. 37. Convolutional Neural Networks/8. Summary.mp47.91MB
  178. 37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp433.24MB
  179. 39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp432.12MB
  180. 39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4112.91MB
  181. 39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp440.79MB
  182. 39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp430.66MB
  183. 39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp429.03MB
  184. 39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp436.74MB
  185. 4. Data Preprocessing in R/10. Data Preprocessing Template.mp450.74MB
  186. 4. Data Preprocessing in R/2. Getting Started.mp49.81MB
  187. 4. Data Preprocessing in R/4. Dataset Description.mp411.85MB
  188. 4. Data Preprocessing in R/5. Importing the Dataset.mp416.42MB
  189. 4. Data Preprocessing in R/6. Taking care of Missing Data.mp439.79MB
  190. 4. Data Preprocessing in R/7. Encoding Categorical Data.mp457.33MB
  191. 4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp486.49MB
  192. 4. Data Preprocessing in R/9. Feature Scaling.mp478.89MB
  193. 40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp426.99MB
  194. 40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4102MB
  195. 40. Linear Discriminant Analysis (LDA)/4. LDA in R.mp451.3MB
  196. 41. Kernel PCA/2. Kernel PCA in Python.mp477.51MB
  197. 41. Kernel PCA/3. Kernel PCA in R.mp456.58MB
  198. 43. Model Selection/2. k-Fold Cross Validation in Python.mp4112.37MB
  199. 43. Model Selection/3. Grid Search in Python.mp4151.79MB
  200. 43. Model Selection/4. k-Fold Cross Validation in R.mp443.64MB
  201. 43. Model Selection/5. Grid Search in R.mp435.55MB
  202. 44. XGBoost/2. XGBoost in Python.mp490MB
  203. 44. XGBoost/4. XGBoost in R.mp447.27MB
  204. 44. XGBoost/5. THANK YOU bonus video.mp452.25MB
  205. 6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp410.53MB
  206. 6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp424.88MB
  207. 6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp411.43MB
  208. 6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp449.17MB
  209. 6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp46MB
  210. 6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp448.61MB
  211. 6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp439.85MB
  212. 6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp428.22MB
  213. 6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp474.58MB
  214. 6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp411.52MB
  215. 7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp412.57MB
  216. 7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp462.34MB
  217. 7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp458.21MB
  218. 7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp472.51MB
  219. 7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp423.45MB
  220. 7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp445.22MB
  221. 7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp413.86MB
  222. 7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp450.79MB
  223. 7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp421.96MB
  224. 7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp42MB
  225. 7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp42.03MB
  226. 7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp416.6MB
  227. 7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp45.34MB
  228. 7. Multiple Linear Regression/6. Understanding the P-Value.mp456.48MB
  229. 7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp432.81MB
  230. 7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp450.92MB
  231. 8. Polynomial Regression/1. Polynomial Regression Intuition.mp49.45MB
  232. 8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp428.52MB
  233. 8. Polynomial Regression/11. R Regression Template.mp431.34MB
  234. 8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp458.25MB
  235. 8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp469.31MB
  236. 8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp477.86MB
  237. 8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp438.79MB
  238. 8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp421.22MB
  239. 8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp432.29MB
  240. 8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp454.81MB
  241. 9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp436.86MB
  242. 9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp419.78MB
  243. 9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp442.57MB
  244. 9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp486.92MB
  245. 9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp434.8MB
  246. 9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp446.3MB
  247. 9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp493.64MB
  248. 9. Support Vector Regression (SVR)/9. SVR in R.mp433.74MB
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