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

  • 收录时间:2019-11-26 23:52:34
  • 文件大小:6GB
  • 下载次数:56
  • 最近下载:2021-01-23 10:29:05
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文件列表

  1. 1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip.zip 228MB
  2. 36. Kernel PCA/3. Kernel PCA in R.mp4 57MB
  3. 1. Welcome to the course!/7. Updates on Udemy Reviews.vtt 53MB
  4. 1. Welcome to the course!/7. Updates on Udemy Reviews.mp4 53MB
  5. 39. XGBoost/5. THANK YOU bonus video.mp4 52MB
  6. 12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 52MB
  7. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51MB
  8. 17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51MB
  9. 18. Random Forest Classification/4. Random Forest Classification in R.mp4 49MB
  10. 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48MB
  11. 39. XGBoost/4. XGBoost in R.mp4 47MB
  12. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47MB
  13. 18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47MB
  14. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 47MB
  15. 7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 47MB
  16. 7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46MB
  17. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45MB
  18. 8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44MB
  19. 16. Naive Bayes/1. Bayes Theorem.mp4 44MB
  20. 24. Apriori/5. Apriori in R - Step 3.mp4 44MB
  21. 38. Model Selection/3. k-Fold Cross Validation in R.mp4 44MB
  22. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 43MB
  23. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43MB
  24. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.vtt 43MB
  25. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 43MB
  26. 24. Apriori/3. Apriori in R - Step 1.vtt 43MB
  27. 24. Apriori/3. Apriori in R - Step 1.mp4 43MB
  28. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43MB
  29. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 43MB
  30. 15. Kernel SVM/6. Kernel SVM in Python.mp4 42MB
  31. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41MB
  32. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 41MB
  33. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41MB
  34. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 41MB
  35. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 41MB
  36. 15. Kernel SVM/7. Kernel SVM in R.mp4 40MB
  37. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 40MB
  38. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.vtt 40MB
  39. 9. Random Forest Regression/4. Random Forest Regression in R.mp4 40MB
  40. 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40MB
  41. 21. K-Means Clustering/5. K-Means Clustering in Python.mp4 40MB
  42. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40MB
  43. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 40MB
  44. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 39MB
  45. 9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39MB
  46. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 39MB
  47. 31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 39MB
  48. 38. Model Selection/4. Grid Search in Python - Step 1.mp4 38MB
  49. 24. Apriori/6. Apriori in Python - Step 1.mp4 38MB
  50. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37MB
  51. 16. Naive Bayes/7. Naive Bayes in R.mp4 37MB
  52. 28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37MB
  53. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.vtt 37MB
  54. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 37MB
  55. 38. Model Selection/6. Grid Search in R.mp4 36MB
  56. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35MB
  57. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35MB
  58. 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4 35MB
  59. 24. Apriori/1. Apriori Intuition.mp4 35MB
  60. 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4 35MB
  61. 8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 34MB
  62. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33MB
  63. 36. Kernel PCA/2. Kernel PCA in Python.mp4 33MB
  64. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33MB
  65. 38. Model Selection/2. k-Fold Cross Validation in Python.mp4 33MB
  66. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 33MB
  67. 14. Support Vector Machine (SVM)/4. SVM in R.mp4 32MB
  68. 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4 32MB
  69. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32MB
  70. 39. XGBoost/3. XGBoost in Python - Step 2.vtt 32MB
  71. 39. XGBoost/3. XGBoost in Python - Step 2.mp4 32MB
  72. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 32MB
  73. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 32MB
  74. 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31MB
  75. 14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31MB
  76. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31MB
  77. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 31MB
  78. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 31MB
  79. 24. Apriori/4. Apriori in R - Step 2.mp4 30MB
  80. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30MB
  81. 31. Artificial Neural Networks/2. The Neuron.mp4 30MB
  82. 17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 30MB
  83. 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4 30MB
  84. 31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 30MB
  85. 38. Model Selection/5. Grid Search in Python - Step 2.mp4 30MB
  86. 24. Apriori/7. Apriori in Python - Step 2.mp4 30MB
  87. 32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29MB
  88. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29MB
  89. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.vtt 29MB
  90. 31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29MB
  91. 15. Kernel SVM/3. The Kernel Trick.mp4 29MB
  92. 12. Logistic Regression/1. Logistic Regression Intuition.mp4 29MB
  93. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29MB
  94. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29MB
  95. 21. K-Means Clustering/6. K-Means Clustering in R.mp4 29MB
  96. 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 29MB
  97. 31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 29MB
  98. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 29MB
  99. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28MB
  100. 16. Naive Bayes/2. Naive Bayes Intuition.mp4 28MB
  101. 6. Polynomial Regression/7. Python Regression Template.mp4 27MB
  102. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27MB
  103. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27MB
  104. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 27MB
  105. 24. Apriori/8. Apriori in Python - Step 3.vtt 27MB
  106. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
  107. 24. Apriori/8. Apriori in Python - Step 3.mp4 27MB
  108. 21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 27MB
  109. 31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27MB
  110. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 26MB
  111. 7. Support Vector Regression (SVR)/4. SVR in R.mp4 26MB
  112. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 26MB
  113. 6. Polynomial Regression/12. R Regression Template.mp4 25MB
  114. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 25MB
  115. 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 25MB
  116. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24MB
  117. 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4 24MB
  118. 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4 24MB
  119. 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4 24MB
  120. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 24MB
  121. 31. Artificial Neural Networks/4. How do Neural Networks work.mp4 24MB
  122. 16. Naive Bayes/6. Naive Bayes in Python.mp4 23MB
  123. 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4 23MB
  124. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23MB
  125. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 23MB
  126. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 23MB
  127. 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.vtt 22MB
  128. 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4 22MB
  129. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22MB
  130. 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4 22MB
  131. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 22MB
  132. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 22MB
  133. 39. XGBoost/2. XGBoost in Python - Step 1.mp4 21MB
  134. 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4 21MB
  135. 25. Eclat/3. Eclat in R.mp4 21MB
  136. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 21MB
  137. 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4 20MB
  138. 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp4 20MB
  139. 18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19MB
  140. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19MB
  141. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19MB
  142. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.vtt 19MB
  143. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 19MB
  144. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 19MB
  145. 19. Evaluating Classification Models Performance/4. CAP Curve.mp4 19MB
  146. 31. Artificial Neural Networks/6. Gradient Descent.mp4 19MB
  147. 31. Artificial Neural Networks/19. ANN in Python - Step 8.vtt 18MB
  148. 31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18MB
  149. 14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18MB
  150. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18MB
  151. 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4 18MB
  152. 1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 18MB
  153. 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 17MB
  154. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17MB
  155. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17MB
  156. 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4 17MB
  157. 31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17MB
  158. 31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 17MB
  159. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17MB
  160. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 17MB
  161. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.vtt 17MB
  162. 31. Artificial Neural Networks/10. Business Problem Description.mp4 16MB
  163. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 16MB
  164. 21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15MB
  165. 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4 15MB
  166. 31. Artificial Neural Networks/3. The Activation Function.mp4 15MB
  167. 12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 15MB
  168. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14MB
  169. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14MB
  170. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14MB
  171. 31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14MB
  172. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14MB
  173. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
  174. 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4 14MB
  175. 9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 14MB
  176. 15. Kernel SVM/2. Mapping to a higher dimension.mp4 14MB
  177. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 14MB
  178. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.vtt 14MB
  179. 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 14MB
  180. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 14MB
  181. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13MB
  182. 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 13MB
  183. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 13MB
  184. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 13MB
  185. 1. Welcome to the course!/3. Why Machine Learning is the Future.mp4 13MB
  186. 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 13MB
  187. 22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 13MB
  188. 12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 13MB
  189. 12. Logistic Regression/14. R Classification Template.mp4 12MB
  190. 15. Kernel SVM/4. Types of Kernel Functions.mp4 12MB
  191. 22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12MB
  192. 12. Logistic Regression/8. Python Classification Template.mp4 12MB
  193. 22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12MB
  194. 14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12MB
  195. 22. Hierarchical Clustering/4. How to get the dataset.mp4 12MB
  196. 24. Apriori/2. How to get the dataset.mp4 12MB
  197. 25. Eclat/2. How to get the dataset.mp4 12MB
  198. 28. Thompson Sampling/3. How to get the dataset.mp4 12MB
  199. 38. Model Selection/1. How to get the dataset.mp4 12MB
  200. 38. Model Selection/1. How to get the dataset.vtt 12MB
  201. 9. Random Forest Regression/2. How to get the dataset.mp4 12MB
  202. 12. Logistic Regression/2. How to get the dataset.mp4 12MB
  203. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 12MB
  204. 15. Kernel SVM/5. How to get the dataset.mp4 12MB
  205. 16. Naive Bayes/5. How to get the dataset.mp4 12MB
  206. 17. Decision Tree Classification/2. How to get the dataset.mp4 12MB
  207. 18. Random Forest Classification/2. How to get the dataset.mp4 12MB
  208. 21. K-Means Clustering/4. How to get the dataset.mp4 12MB
  209. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12MB
  210. 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4 12MB
  211. 31. Artificial Neural Networks/9. How to get the dataset.mp4 12MB
  212. 32. Convolutional Neural Networks/10. How to get the dataset.mp4 12MB
  213. 34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 12MB
  214. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 12MB
  215. 36. Kernel PCA/1. How to get the dataset.mp4 12MB
  216. 39. XGBoost/1. How to get the dataset.mp4 12MB
  217. 4. Simple Linear Regression/1. How to get the dataset.mp4 12MB
  218. 5. Multiple Linear Regression/1. How to get the dataset.mp4 12MB
  219. 6. Polynomial Regression/2. How to get the dataset.mp4 12MB
  220. 7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 12MB
  221. 8. Decision Tree Regression/2. How to get the dataset.mp4 12MB
  222. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12MB
  223. 22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11MB
  224. 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4 11MB
  225. 31. Artificial Neural Networks/8. Backpropagation.mp4 11MB
  226. 22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 11MB
  227. 25. Eclat/1. Eclat Intuition.mp4 11MB
  228. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10MB
  229. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10MB
  230. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10MB
  231. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 10MB
  232. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 10MB
  233. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 10MB
  234. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9MB
  235. 6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9MB
  236. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9MB
  237. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9MB
  238. 31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9MB
  239. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9MB
  240. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 9MB
  241. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8MB
  242. 22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8MB
  243. 31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8MB
  244. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8MB
  245. 19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8MB
  246. 1. Welcome to the course!/1. Applications of Machine Learning.mp4 8MB
  247. 32. Convolutional Neural Networks/8. Summary.mp4 8MB
  248. 12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 8MB
  249. 22. Hierarchical Clustering/12. HC in R - Step 3.mp4 8MB
  250. 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 8MB
  251. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7MB
  252. 22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7MB
  253. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7MB
  254. 22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7MB
  255. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7MB
  256. 31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7MB
  257. 12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 7MB
  258. 22. Hierarchical Clustering/14. HC in R - Step 5.mp4 7MB
  259. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 7MB
  260. 4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 7MB
  261. 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 7MB
  262. 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4 6MB
  263. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6MB
  264. 32. Convolutional Neural Networks/1. Plan of attack.mp4 6MB
  265. 31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 6MB
  266. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 6MB
  267. 15. Kernel SVM/1. Kernel SVM Intuition.mp4 6MB
  268. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5MB
  269. 31. Artificial Neural Networks/1. Plan of attack.mp4 5MB
  270. 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 5MB
  271. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 5MB
  272. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4MB
  273. 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4 3MB
  274. 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.vtt 3MB
  275. 32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3MB
  276. 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4 3MB
  277. 1. Welcome to the course!/5.1 Machine_Learning_A_Z_Q_A.pdf.pdf 2MB
  278. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.vtt 2MB
  279. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2MB
  280. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 2MB
  281. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 2MB
  282. 25. Eclat/3.1 Eclat.zip.zip 49KB
  283. 16. Naive Bayes/1. Bayes Theorem.vtt 31KB
  284. 18. Random Forest Classification/4. Random Forest Classification in R.vtt 29KB
  285. 8. Decision Tree Regression/4. Decision Tree Regression in R.vtt 29KB
  286. 24. Apriori/5. Apriori in R - Step 3.vtt 28KB
  287. 7. Support Vector Regression (SVR)/3. SVR in Python.vtt 27KB
  288. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.vtt 27KB
  289. 18. Random Forest Classification/3. Random Forest Classification in Python.vtt 27KB
  290. 36. Kernel PCA/3. Kernel PCA in R.vtt 27KB
  291. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.vtt 26KB
  292. 12. Logistic Regression/13. Logistic Regression in R - Step 5.vtt 26KB
  293. 17. Decision Tree Classification/4. Decision Tree Classification in R.vtt 26KB
  294. 22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf.pdf 26KB
  295. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.vtt 26KB
  296. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.vtt 25KB
  297. 21. K-Means Clustering/5. K-Means Clustering in Python.vtt 25KB
  298. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.vtt 25KB
  299. 9. Random Forest Regression/4. Random Forest Regression in R.vtt 25KB
  300. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.vtt 25KB
  301. 15. Kernel SVM/6. Kernel SVM in Python.vtt 25KB
  302. 24. Apriori/6. Apriori in Python - Step 1.vtt 25KB
  303. 31. Artificial Neural Networks/13. ANN in Python - Step 2.vtt 25KB
  304. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt 25KB
  305. 9. Random Forest Regression/3. Random Forest Regression in Python.vtt 24KB
  306. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.vtt 24KB
  307. 38. Model Selection/3. k-Fold Cross Validation in R.vtt 24KB
  308. 28. Thompson Sampling/1. Thompson Sampling Intuition.vtt 24KB
  309. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.vtt 24KB
  310. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.vtt 24KB
  311. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.vtt 23KB
  312. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.vtt 23KB
  313. 31. Artificial Neural Networks/22. ANN in R - Step 1.vtt 23KB
  314. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.vtt 23KB
  315. 15. Kernel SVM/7. Kernel SVM in R.vtt 23KB
  316. 24. Apriori/1. Apriori Intuition.vtt 23KB
  317. 39. XGBoost/4. XGBoost in R.vtt 23KB
  318. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.vtt 22KB
  319. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.vtt 22KB
  320. 31. Artificial Neural Networks/2. The Neuron.vtt 22KB
  321. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.vtt 22KB
  322. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.vtt 22KB
  323. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.vtt 21KB
  324. 8. Decision Tree Regression/3. Decision Tree Regression in Python.vtt 21KB
  325. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.vtt 21KB
  326. 21. K-Means Clustering/1. K-Means Clustering Intuition.vtt 21KB
  327. 12. Logistic Regression/1. Logistic Regression Intuition.vtt 21KB
  328. 16. Naive Bayes/2. Naive Bayes Intuition.vtt 21KB
  329. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.vtt 21KB
  330. 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.vtt 21KB
  331. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.vtt 21KB
  332. 24. Apriori/4. Apriori in R - Step 2.vtt 21KB
  333. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.vtt 20KB
  334. 24. Apriori/7. Apriori in Python - Step 2.vtt 20KB
  335. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.vtt 20KB
  336. 16. Naive Bayes/7. Naive Bayes in R.vtt 19KB
  337. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.vtt 19KB
  338. 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.vtt 19KB
  339. 32. Convolutional Neural Networks/2. What are convolutional neural networks.vtt 19KB
  340. 38. Model Selection/4. Grid Search in Python - Step 1.vtt 19KB
  341. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.vtt 19KB
  342. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.vtt 19KB
  343. 36. Kernel PCA/2. Kernel PCA in Python.vtt 19KB
  344. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.vtt 19KB
  345. 32. Convolutional Neural Networks/5. Step 2 - Pooling.vtt 18KB
  346. 38. Model Selection/6. Grid Search in R.vtt 18KB
  347. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).vtt 18KB
  348. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.vtt 18KB
  349. 38. Model Selection/2. k-Fold Cross Validation in Python.vtt 18KB
  350. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.vtt 18KB
  351. 31. Artificial Neural Networks/12. ANN in Python - Step 1.vtt 17KB
  352. 21. K-Means Clustering/6. K-Means Clustering in R.vtt 17KB
  353. 17. Decision Tree Classification/3. Decision Tree Classification in Python.vtt 17KB
  354. 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.vtt 17KB
  355. 31. Artificial Neural Networks/16. ANN in Python - Step 5.vtt 17KB
  356. 14. Support Vector Machine (SVM)/3. SVM in Python.vtt 17KB
  357. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.vtt 17KB
  358. 31. Artificial Neural Networks/4. How do Neural Networks work.vtt 17KB
  359. 6. Polynomial Regression/12. R Regression Template.vtt 17KB
  360. 7. Support Vector Regression (SVR)/4. SVR in R.vtt 17KB
  361. 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.vtt 17KB
  362. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.vtt 17KB
  363. 31. Artificial Neural Networks/5. How do Neural Networks learn.vtt 17KB
  364. 31. Artificial Neural Networks/24. ANN in R - Step 3.vtt 16KB
  365. 14. Support Vector Machine (SVM)/4. SVM in R.vtt 16KB
  366. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.vtt 16KB
  367. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.vtt 16KB
  368. 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.vtt 16KB
  369. 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.vtt 16KB
  370. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.vtt 16KB
  371. 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.vtt 16KB
  372. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.vtt 15KB
  373. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.vtt 15KB
  374. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.vtt 15KB
  375. 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.vtt 15KB
  376. 6. Polynomial Regression/7. Python Regression Template.vtt 15KB
  377. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.vtt 15KB
  378. 19. Evaluating Classification Models Performance/4. CAP Curve.vtt 15KB
  379. 15. Kernel SVM/3. The Kernel Trick.vtt 14KB
  380. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).vtt 14KB
  381. 14. Support Vector Machine (SVM)/1. SVM Intuition.vtt 14KB
  382. 25. Eclat/3. Eclat in R.vtt 14KB
  383. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.vtt 14KB
  384. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.vtt 14KB
  385. 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.vtt 14KB
  386. 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.vtt 14KB
  387. 38. Model Selection/5. Grid Search in Python - Step 2.vtt 13KB
  388. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.vtt 13KB
  389. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.vtt 13KB
  390. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.vtt 13KB
  391. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.vtt 13KB
  392. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.vtt 13KB
  393. 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.vtt 13KB
  394. 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.vtt 13KB
  395. 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.vtt 12KB
  396. 31. Artificial Neural Networks/6. Gradient Descent.vtt 12KB
  397. 16. Naive Bayes/6. Naive Bayes in Python.vtt 12KB
  398. 39. XGBoost/2. XGBoost in Python - Step 1.vtt 12KB
  399. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.vtt 12KB
  400. 21. K-Means Clustering/2. K-Means Random Initialization Trap.vtt 12KB
  401. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.vtt 12KB
  402. 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.vtt 11KB
  403. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.vtt 11KB
  404. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.vtt 11KB
  405. 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).vtt 11KB
  406. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.vtt 11KB
  407. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.vtt 11KB
  408. 31. Artificial Neural Networks/3. The Activation Function.vtt 11KB
  409. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.vtt 11KB
  410. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.vtt 10KB
  411. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.vtt 10KB
  412. 7. Support Vector Regression (SVR)/2. SVR Intuition.vtt 10KB
  413. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.vtt 10KB
  414. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.vtt 10KB
  415. 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.vtt 9KB
  416. 15. Kernel SVM/2. Mapping to a higher dimension.vtt 9KB
  417. 9. Random Forest Regression/1. Random Forest Regression Intuition.vtt 9KB
  418. 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.vtt 9KB
  419. 31. Artificial Neural Networks/21. ANN in Python - Step 10.vtt 9KB
  420. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.vtt 9KB
  421. 31. Artificial Neural Networks/23. ANN in R - Step 2.vtt 9KB
  422. 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.vtt 9KB
  423. 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.vtt 9KB
  424. 22. Hierarchical Clustering/6. HC in Python - Step 2.vtt 9KB
  425. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).vtt 9KB
  426. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.vtt 8KB
  427. 14. Support Vector Machine (SVM)/4.1 SVM.zip.zip 8KB
  428. 31. Artificial Neural Networks/20. ANN in Python - Step 9.vtt 8KB
  429. 1. Welcome to the course!/3. Why Machine Learning is the Future.vtt 8KB
  430. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.vtt 8KB
  431. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.vtt 8KB
  432. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.vtt 8KB
  433. 1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).vtt 8KB
  434. 12. Logistic Regression/9. Logistic Regression in R - Step 1.vtt 8KB
  435. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.vtt 8KB
  436. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.vtt 7KB
  437. 22. Hierarchical Clustering/11. HC in R - Step 2.vtt 7KB
  438. 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.vtt 7KB
  439. 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.vtt 7KB
  440. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.vtt 7KB
  441. 25. Eclat/1. Eclat Intuition.vtt 7KB
  442. 6. Polynomial Regression/1. Polynomial Regression Intuition.vtt 7KB
  443. 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.vtt 7KB
  444. 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.vtt 7KB
  445. 22. Hierarchical Clustering/7. HC in Python - Step 3.vtt 7KB
  446. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.vtt 7KB
  447. 22. Hierarchical Clustering/5. HC in Python - Step 1.vtt 7KB
  448. 19. Evaluating Classification Models Performance/2. Confusion Matrix.vtt 7KB
  449. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.vtt 7KB
  450. 12. Logistic Regression/11. Logistic Regression in R - Step 3.vtt 7KB
  451. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.vtt 7KB
  452. 31. Artificial Neural Networks/10. Business Problem Description.vtt 6KB
  453. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.vtt 6KB
  454. 18. Random Forest Classification/1. Random Forest Classification Intuition.vtt 6KB
  455. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.vtt 6KB
  456. 31. Artificial Neural Networks/8. Backpropagation.vtt 6KB
  457. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.vtt 6KB
  458. 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.vtt 6KB
  459. 22. Hierarchical Clustering/9. HC in Python - Step 5.vtt 6KB
  460. 12. Logistic Regression/14. R Classification Template.vtt 6KB
  461. 22. Hierarchical Clustering/8. HC in Python - Step 4.vtt 6KB
  462. 22. Hierarchical Clustering/10. HC in R - Step 1.vtt 6KB
  463. 12. Logistic Regression/8. Python Classification Template.vtt 5KB
  464. 32. Convolutional Neural Networks/8. Summary.vtt 5KB
  465. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.vtt 5KB
  466. 31. Artificial Neural Networks/18. ANN in Python - Step 7.vtt 5KB
  467. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.vtt 5KB
  468. 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.vtt 5KB
  469. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.vtt 5KB
  470. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.vtt 5KB
  471. 1. Welcome to the course!/1. Applications of Machine Learning.vtt 5KB
  472. 32. Convolutional Neural Networks/1. Plan of attack.vtt 5KB
  473. 31. Artificial Neural Networks/14. ANN in Python - Step 3.vtt 5KB
  474. 40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 5KB
  475. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.vtt 5KB
  476. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.vtt 4KB
  477. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.vtt 4KB
  478. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.vtt 4KB
  479. 15. Kernel SVM/4. Types of Kernel Functions.vtt 4KB
  480. 22. Hierarchical Clustering/12. HC in R - Step 3.vtt 4KB
  481. 12. Logistic Regression/2. How to get the dataset.vtt 4KB
  482. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.vtt 4KB
  483. 14. Support Vector Machine (SVM)/2. How to get the dataset.vtt 4KB
  484. 15. Kernel SVM/5. How to get the dataset.vtt 4KB
  485. 16. Naive Bayes/5. How to get the dataset.vtt 4KB
  486. 17. Decision Tree Classification/2. How to get the dataset.vtt 4KB
  487. 18. Random Forest Classification/2. How to get the dataset.vtt 4KB
  488. 21. K-Means Clustering/4. How to get the dataset.vtt 4KB
  489. 22. Hierarchical Clustering/4. How to get the dataset.vtt 4KB
  490. 24. Apriori/2. How to get the dataset.vtt 4KB
  491. 25. Eclat/2. How to get the dataset.vtt 4KB
  492. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.vtt 4KB
  493. 28. Thompson Sampling/3. How to get the dataset.vtt 4KB
  494. 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.vtt 4KB
  495. 31. Artificial Neural Networks/9. How to get the dataset.vtt 4KB
  496. 32. Convolutional Neural Networks/10. How to get the dataset.vtt 4KB
  497. 34. Principal Component Analysis (PCA)/2. How to get the dataset.vtt 4KB
  498. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.vtt 4KB
  499. 36. Kernel PCA/1. How to get the dataset.vtt 4KB
  500. 39. XGBoost/1. How to get the dataset.vtt 4KB
  501. 4. Simple Linear Regression/1. How to get the dataset.vtt 4KB
  502. 5. Multiple Linear Regression/1. How to get the dataset.vtt 4KB
  503. 6. Polynomial Regression/2. How to get the dataset.vtt 4KB
  504. 7. Support Vector Regression (SVR)/1. How to get the dataset.vtt 4KB
  505. 8. Decision Tree Regression/2. How to get the dataset.vtt 4KB
  506. 9. Random Forest Regression/2. How to get the dataset.vtt 4KB
  507. 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.vtt 4KB
  508. 31. Artificial Neural Networks/17. ANN in Python - Step 6.vtt 4KB
  509. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.vtt 4KB
  510. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.vtt 4KB
  511. 12. Logistic Regression/10. Logistic Regression in R - Step 2.vtt 4KB
  512. 15. Kernel SVM/1. Kernel SVM Intuition.vtt 4KB
  513. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.vtt 4KB
  514. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.vtt 4KB
  515. 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.vtt 4KB
  516. 19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 4KB
  517. 4. Simple Linear Regression/2. Dataset + Business Problem Description.vtt 4KB
  518. 22. Hierarchical Clustering/14. HC in R - Step 5.vtt 4KB
  519. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.vtt 4KB
  520. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.vtt 4KB
  521. 12. Logistic Regression/12. Logistic Regression in R - Step 4.vtt 4KB
  522. 31. Artificial Neural Networks/1. Plan of attack.vtt 4KB
  523. 22. Hierarchical Clustering/13. HC in R - Step 4.vtt 3KB
  524. 31. Artificial Neural Networks/15. ANN in Python - Step 4.vtt 3KB
  525. 1. Welcome to the course!/4. Important notes, tips & tricks for this course.html 3KB
  526. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.vtt 3KB
  527. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.vtt 3KB
  528. 10. Evaluating Regression Models Performance/5. Conclusion of Part 2 - Regression.html 3KB
  529. 2. -------------------- Part 1 Data Preprocessing --------------------/8. WARNING - Update.html 3KB
  530. 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.vtt 3KB
  531. 32. Convolutional Neural Networks/22. CNN in R.html 2KB
  532. 1. Welcome to the course!/2. BONUS Learning Paths.html 2KB
  533. 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.vtt 2KB
  534. 32. Convolutional Neural Networks/6. Step 3 - Flattening.vtt 2KB
  535. 5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html 2KB
  536. 39. XGBoost/5. THANK YOU bonus video.vtt 2KB
  537. 1. Welcome to the course!/13. FAQBot!.html 2KB
  538. 29. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html 2KB
  539. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.vtt 2KB
  540. 1. Welcome to the course!/5. This PDF resource will help you a lot.html 1KB
  541. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.vtt 1KB
  542. 31. Artificial Neural Networks/11. Installing Keras.html 1KB
  543. 29. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html 1KB
  544. 29. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1KB
  545. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.vtt 1KB
  546. 1. Welcome to the course!/9. Update Recommended Anaconda Version.html 1KB
  547. 33. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1KB
  548. 1. Welcome to the course!/11. BONUS Meet your instructors.html 1KB
  549. 1. Welcome to the course!/6. The whole code folder of the course.html 1KB
  550. 32. Convolutional Neural Networks/11. Installing Keras.html 927B
  551. 37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899B
  552. 3. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875B
  553. 30. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870B
  554. 11. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html 831B
  555. 26. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 804B
  556. 20. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734B
  557. 5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html 726B
  558. 5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676B
  559. 1. Welcome to the course!/12. Some Additional Resources.html 551B
  560. 22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 516B
  561. 23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425B
  562. courseupload.com.webloc 248B
  563. 12. Logistic Regression/15. Logistic Regression.html 118B
  564. 13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 118B
  565. 2. -------------------- Part 1 Data Preprocessing --------------------/12. Data Preprocessing.html 118B
  566. 21. K-Means Clustering/7. K-Means Clustering.html 118B
  567. 22. Hierarchical Clustering/15. Hierarchical Clustering.html 118B
  568. 4. Simple Linear Regression/13. Simple Linear Regression.html 118B
  569. 5. Multiple Linear Regression/22. Multiple Linear Regression.html 118B