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

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  • 文件大小:6GB
  • 下载次数:281
  • 最近下载:2021-01-23 10:11:50
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文件列表

  1. 36. Kernel PCA/3. Kernel PCA in R.mp4 57MB
  2. 1. Welcome to the course!/5. Updates on Udemy Reviews.mp4 53MB
  3. 12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 52MB
  4. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51MB
  5. 17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51MB
  6. 18. Random Forest Classification/4. Random Forest Classification in R.mp4 49MB
  7. 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48MB
  8. 39. XGBoost/4. XGBoost in R.mp4 47MB
  9. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47MB
  10. 18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47MB
  11. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 47MB
  12. 7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 47MB
  13. 7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46MB
  14. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45MB
  15. 8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44MB
  16. 16. Naive Bayes/1. Bayes Theorem.mp4 44MB
  17. 24. Apriori/5. Apriori in R - Step 3.mp4 44MB
  18. 38. Model Selection/3. k-Fold Cross Validation in R.mp4 44MB
  19. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 43MB
  20. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43MB
  21. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 43MB
  22. 24. Apriori/3. Apriori in R - Step 1.mp4 43MB
  23. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43MB
  24. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 43MB
  25. 15. Kernel SVM/6. Kernel SVM in Python.mp4 42MB
  26. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41MB
  27. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 41MB
  28. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41MB
  29. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 41MB
  30. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 41MB
  31. 15. Kernel SVM/7. Kernel SVM in R.mp4 40MB
  32. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 40MB
  33. 9. Random Forest Regression/4. Random Forest Regression in R.mp4 40MB
  34. 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40MB
  35. 21. K-Means Clustering/5. K-Means Clustering in Python.mp4 40MB
  36. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40MB
  37. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 40MB
  38. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 39MB
  39. 9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39MB
  40. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 39MB
  41. 31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 39MB
  42. 38. Model Selection/4. Grid Search in Python - Step 1.mp4 38MB
  43. 24. Apriori/6. Apriori in Python - Step 1.mp4 38MB
  44. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37MB
  45. 16. Naive Bayes/7. Naive Bayes in R.mp4 37MB
  46. 28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37MB
  47. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 37MB
  48. 38. Model Selection/6. Grid Search in R.mp4 36MB
  49. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35MB
  50. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35MB
  51. 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4 35MB
  52. 24. Apriori/1. Apriori Intuition.mp4 35MB
  53. 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4 35MB
  54. 8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 34MB
  55. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33MB
  56. 36. Kernel PCA/2. Kernel PCA in Python.mp4 33MB
  57. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33MB
  58. 38. Model Selection/2. k-Fold Cross Validation in Python.mp4 33MB
  59. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 33MB
  60. 14. Support Vector Machine (SVM)/4. SVM in R.mp4 32MB
  61. 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4 32MB
  62. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32MB
  63. 39. XGBoost/3. XGBoost in Python - Step 2.mp4 32MB
  64. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 32MB
  65. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 32MB
  66. 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31MB
  67. 14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31MB
  68. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31MB
  69. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 31MB
  70. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 31MB
  71. 24. Apriori/4. Apriori in R - Step 2.mp4 30MB
  72. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30MB
  73. 31. Artificial Neural Networks/2. The Neuron.mp4 30MB
  74. 17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 30MB
  75. 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4 30MB
  76. 31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 30MB
  77. 24. Apriori/7. Apriori in Python - Step 2.mp4 30MB
  78. 38. Model Selection/5. Grid Search in Python - Step 2.mp4 30MB
  79. 32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 30MB
  80. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29MB
  81. 31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29MB
  82. 15. Kernel SVM/3. The Kernel Trick.mp4 29MB
  83. 12. Logistic Regression/1. Logistic Regression Intuition.mp4 29MB
  84. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29MB
  85. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29MB
  86. 21. K-Means Clustering/6. K-Means Clustering in R.mp4 29MB
  87. 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 29MB
  88. 31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 29MB
  89. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 29MB
  90. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28MB
  91. 16. Naive Bayes/2. Naive Bayes Intuition.mp4 28MB
  92. 6. Polynomial Regression/7. Python Regression Template.mp4 27MB
  93. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27MB
  94. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27MB
  95. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 27MB
  96. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
  97. 24. Apriori/8. Apriori in Python - Step 3.mp4 27MB
  98. 21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 27MB
  99. 31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27MB
  100. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 26MB
  101. 7. Support Vector Regression (SVR)/4. SVR in R.mp4 26MB
  102. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 26MB
  103. 6. Polynomial Regression/12. R Regression Template.mp4 25MB
  104. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 25MB
  105. 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 25MB
  106. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24MB
  107. 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4 24MB
  108. 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4 24MB
  109. 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4 24MB
  110. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 24MB
  111. 31. Artificial Neural Networks/4. How do Neural Networks work.mp4 24MB
  112. 16. Naive Bayes/6. Naive Bayes in Python.mp4 23MB
  113. 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4 23MB
  114. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23MB
  115. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 23MB
  116. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 23MB
  117. 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4 22MB
  118. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22MB
  119. 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4 22MB
  120. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 22MB
  121. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 22MB
  122. 39. XGBoost/2. XGBoost in Python - Step 1.mp4 21MB
  123. 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4 21MB
  124. 25. Eclat/3. Eclat in R.mp4 21MB
  125. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 21MB
  126. 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4 20MB
  127. 1. Welcome to the course!/6. Installing Python and Anaconda (Mac, Linux & Windows).mp4 20MB
  128. 18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19MB
  129. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19MB
  130. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19MB
  131. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 19MB
  132. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 19MB
  133. 19. Evaluating Classification Models Performance/4. CAP Curve.mp4 19MB
  134. 31. Artificial Neural Networks/6. Gradient Descent.mp4 19MB
  135. 31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18MB
  136. 14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18MB
  137. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18MB
  138. 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4 18MB
  139. 1. Welcome to the course!/8. Installing R and R Studio (Mac, Linux & Windows).mp4 18MB
  140. 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 17MB
  141. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17MB
  142. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17MB
  143. 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4 17MB
  144. 31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17MB
  145. 31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 17MB
  146. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17MB
  147. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 17MB
  148. 31. Artificial Neural Networks/10. Business Problem Description.mp4 16MB
  149. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 16MB
  150. 21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15MB
  151. 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4 15MB
  152. 31. Artificial Neural Networks/3. The Activation Function.mp4 15MB
  153. 12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 15MB
  154. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14MB
  155. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14MB
  156. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14MB
  157. 31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14MB
  158. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14MB
  159. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
  160. 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4 14MB
  161. 9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 14MB
  162. 15. Kernel SVM/2. Mapping to a higher dimension.mp4 14MB
  163. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 14MB
  164. 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 14MB
  165. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 14MB
  166. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.vtt 13MB
  167. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13MB
  168. 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 13MB
  169. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 13MB
  170. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 13MB
  171. 1. Welcome to the course!/2. Why Machine Learning is the Future.mp4 13MB
  172. 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 13MB
  173. 22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 13MB
  174. 12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 13MB
  175. 12. Logistic Regression/14. R Classification Template.mp4 12MB
  176. 22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12MB
  177. 15. Kernel SVM/4. Types of Kernel Functions.mp4 12MB
  178. 12. Logistic Regression/8. Python Classification Template.mp4 12MB
  179. 22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12MB
  180. 14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12MB
  181. 18. Random Forest Classification/2. How to get the dataset.mp4 12MB
  182. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12MB
  183. 28. Thompson Sampling/3. How to get the dataset.mp4 12MB
  184. 32. Convolutional Neural Networks/10. How to get the dataset.mp4 12MB
  185. 34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 12MB
  186. 36. Kernel PCA/1. How to get the dataset.mp4 12MB
  187. 39. XGBoost/1. How to get the dataset.mp4 12MB
  188. 4. Simple Linear Regression/1. How to get the dataset.mp4 12MB
  189. 6. Polynomial Regression/2. How to get the dataset.mp4 12MB
  190. 7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 12MB
  191. 8. Decision Tree Regression/2. How to get the dataset.mp4 12MB
  192. 9. Random Forest Regression/2. How to get the dataset.mp4 12MB
  193. 12. Logistic Regression/2. How to get the dataset.mp4 12MB
  194. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 12MB
  195. 15. Kernel SVM/5. How to get the dataset.mp4 12MB
  196. 16. Naive Bayes/5. How to get the dataset.mp4 12MB
  197. 17. Decision Tree Classification/2. How to get the dataset.mp4 12MB
  198. 21. K-Means Clustering/4. How to get the dataset.mp4 12MB
  199. 22. Hierarchical Clustering/4. How to get the dataset.mp4 12MB
  200. 24. Apriori/2. How to get the dataset.mp4 12MB
  201. 25. Eclat/2. How to get the dataset.mp4 12MB
  202. 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4 12MB
  203. 31. Artificial Neural Networks/9. How to get the dataset.mp4 12MB
  204. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 12MB
  205. 38. Model Selection/1. How to get the dataset.mp4 12MB
  206. 5. Multiple Linear Regression/1. How to get the dataset.mp4 12MB
  207. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12MB
  208. 22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11MB
  209. 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4 11MB
  210. 31. Artificial Neural Networks/8. Backpropagation.mp4 11MB
  211. 22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 11MB
  212. 25. Eclat/1. Eclat Intuition.mp4 11MB
  213. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10MB
  214. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10MB
  215. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10MB
  216. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 10MB
  217. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 10MB
  218. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 10MB
  219. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9MB
  220. 6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9MB
  221. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9MB
  222. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9MB
  223. 31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9MB
  224. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9MB
  225. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 9MB
  226. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8MB
  227. 22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8MB
  228. 31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8MB
  229. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8MB
  230. 19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8MB
  231. 1. Welcome to the course!/1. Applications of Machine Learning.mp4 8MB
  232. 32. Convolutional Neural Networks/8. Summary.mp4 8MB
  233. 12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 8MB
  234. 22. Hierarchical Clustering/12. HC in R - Step 3.mp4 8MB
  235. 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 8MB
  236. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7MB
  237. 22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7MB
  238. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7MB
  239. 22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7MB
  240. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7MB
  241. 31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7MB
  242. 12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 7MB
  243. 22. Hierarchical Clustering/14. HC in R - Step 5.mp4 7MB
  244. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 7MB
  245. 4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 7MB
  246. 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 7MB
  247. 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4 6MB
  248. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6MB
  249. 32. Convolutional Neural Networks/1. Plan of attack.mp4 6MB
  250. 31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 6MB
  251. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 6MB
  252. 15. Kernel SVM/1. Kernel SVM Intuition.mp4 6MB
  253. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5MB
  254. 31. Artificial Neural Networks/1. Plan of attack.mp4 5MB
  255. 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 5MB
  256. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 5MB
  257. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4MB
  258. 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4 3MB
  259. 32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3MB
  260. 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4 3MB
  261. 1. Welcome to the course!/4.1 Machine_Learning_A_Z_Q_A.pdf.pdf 2MB
  262. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2MB
  263. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 2MB
  264. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 2MB
  265. 25. Eclat/3.1 Eclat.zip.zip 49KB
  266. 16. Naive Bayes/1. Bayes Theorem.vtt 31KB
  267. 18. Random Forest Classification/4. Random Forest Classification in R.vtt 29KB
  268. 8. Decision Tree Regression/4. Decision Tree Regression in R.vtt 29KB
  269. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.vtt 28KB
  270. 24. Apriori/5. Apriori in R - Step 3.vtt 28KB
  271. 24. Apriori/3. Apriori in R - Step 1.vtt 28KB
  272. 7. Support Vector Regression (SVR)/3. SVR in Python.vtt 27KB
  273. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.vtt 27KB
  274. 18. Random Forest Classification/3. Random Forest Classification in Python.vtt 27KB
  275. 36. Kernel PCA/3. Kernel PCA in R.vtt 27KB
  276. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.vtt 26KB
  277. 12. Logistic Regression/13. Logistic Regression in R - Step 5.vtt 26KB
  278. 17. Decision Tree Classification/4. Decision Tree Classification in R.vtt 26KB
  279. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.vtt 26KB
  280. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.vtt 25KB
  281. 21. K-Means Clustering/5. K-Means Clustering in Python.vtt 25KB
  282. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.vtt 25KB
  283. 9. Random Forest Regression/4. Random Forest Regression in R.vtt 25KB
  284. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.vtt 25KB
  285. 15. Kernel SVM/6. Kernel SVM in Python.vtt 25KB
  286. 24. Apriori/6. Apriori in Python - Step 1.vtt 25KB
  287. 31. Artificial Neural Networks/13. ANN in Python - Step 2.vtt 25KB
  288. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt 25KB
  289. 9. Random Forest Regression/3. Random Forest Regression in Python.vtt 24KB
  290. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.vtt 24KB
  291. 38. Model Selection/3. k-Fold Cross Validation in R.vtt 24KB
  292. 28. Thompson Sampling/1. Thompson Sampling Intuition.vtt 24KB
  293. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.vtt 24KB
  294. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.vtt 24KB
  295. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.vtt 23KB
  296. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.vtt 23KB
  297. 31. Artificial Neural Networks/22. ANN in R - Step 1.vtt 23KB
  298. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.vtt 23KB
  299. 15. Kernel SVM/7. Kernel SVM in R.vtt 23KB
  300. 24. Apriori/1. Apriori Intuition.vtt 23KB
  301. 39. XGBoost/4. XGBoost in R.vtt 23KB
  302. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.vtt 22KB
  303. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.vtt 22KB
  304. 31. Artificial Neural Networks/2. The Neuron.vtt 22KB
  305. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.vtt 22KB
  306. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.vtt 22KB
  307. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.vtt 21KB
  308. 8. Decision Tree Regression/3. Decision Tree Regression in Python.vtt 21KB
  309. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.vtt 21KB
  310. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.vtt 21KB
  311. 21. K-Means Clustering/1. K-Means Clustering Intuition.vtt 21KB
  312. 12. Logistic Regression/1. Logistic Regression Intuition.vtt 21KB
  313. 16. Naive Bayes/2. Naive Bayes Intuition.vtt 21KB
  314. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.vtt 21KB
  315. 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.vtt 21KB
  316. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.vtt 21KB
  317. 24. Apriori/4. Apriori in R - Step 2.vtt 21KB
  318. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.vtt 20KB
  319. 24. Apriori/7. Apriori in Python - Step 2.vtt 20KB
  320. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.vtt 20KB
  321. 16. Naive Bayes/7. Naive Bayes in R.vtt 19KB
  322. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.vtt 19KB
  323. 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.vtt 19KB
  324. 32. Convolutional Neural Networks/2. What are convolutional neural networks.vtt 19KB
  325. 38. Model Selection/4. Grid Search in Python - Step 1.vtt 19KB
  326. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.vtt 19KB
  327. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.vtt 19KB
  328. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.vtt 19KB
  329. 36. Kernel PCA/2. Kernel PCA in Python.vtt 19KB
  330. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.vtt 19KB
  331. 32. Convolutional Neural Networks/5. Step 2 - Pooling.vtt 18KB
  332. 38. Model Selection/6. Grid Search in R.vtt 18KB
  333. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).vtt 18KB
  334. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.vtt 18KB
  335. 38. Model Selection/2. k-Fold Cross Validation in Python.vtt 18KB
  336. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.vtt 18KB
  337. 31. Artificial Neural Networks/12. ANN in Python - Step 1.vtt 17KB
  338. 24. Apriori/8. Apriori in Python - Step 3.vtt 17KB
  339. 21. K-Means Clustering/6. K-Means Clustering in R.vtt 17KB
  340. 17. Decision Tree Classification/3. Decision Tree Classification in Python.vtt 17KB
  341. 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.vtt 17KB
  342. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.vtt 17KB
  343. 31. Artificial Neural Networks/16. ANN in Python - Step 5.vtt 17KB
  344. 14. Support Vector Machine (SVM)/3. SVM in Python.vtt 17KB
  345. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.vtt 17KB
  346. 31. Artificial Neural Networks/4. How do Neural Networks work.vtt 17KB
  347. 6. Polynomial Regression/12. R Regression Template.vtt 17KB
  348. 7. Support Vector Regression (SVR)/4. SVR in R.vtt 17KB
  349. 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.vtt 17KB
  350. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.vtt 17KB
  351. 31. Artificial Neural Networks/5. How do Neural Networks learn.vtt 17KB
  352. 39. XGBoost/3. XGBoost in Python - Step 2.vtt 16KB
  353. 31. Artificial Neural Networks/24. ANN in R - Step 3.vtt 16KB
  354. 14. Support Vector Machine (SVM)/4. SVM in R.vtt 16KB
  355. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.vtt 16KB
  356. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.vtt 16KB
  357. 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.vtt 16KB
  358. 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.vtt 16KB
  359. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.vtt 16KB
  360. 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.vtt 16KB
  361. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.vtt 15KB
  362. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.vtt 15KB
  363. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.vtt 15KB
  364. 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.vtt 15KB
  365. 6. Polynomial Regression/7. Python Regression Template.vtt 15KB
  366. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.vtt 15KB
  367. 19. Evaluating Classification Models Performance/4. CAP Curve.vtt 15KB
  368. 15. Kernel SVM/3. The Kernel Trick.vtt 14KB
  369. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).vtt 14KB
  370. 14. Support Vector Machine (SVM)/1. SVM Intuition.vtt 14KB
  371. 25. Eclat/3. Eclat in R.vtt 14KB
  372. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.vtt 14KB
  373. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.vtt 14KB
  374. 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.vtt 14KB
  375. 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.vtt 14KB
  376. 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.vtt 14KB
  377. 38. Model Selection/5. Grid Search in Python - Step 2.vtt 13KB
  378. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.vtt 13KB
  379. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.vtt 13KB
  380. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.vtt 13KB
  381. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.vtt 13KB
  382. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.vtt 13KB
  383. 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.vtt 13KB
  384. 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.vtt 13KB
  385. 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.vtt 12KB
  386. 31. Artificial Neural Networks/6. Gradient Descent.vtt 12KB
  387. 16. Naive Bayes/6. Naive Bayes in Python.vtt 12KB
  388. 39. XGBoost/2. XGBoost in Python - Step 1.vtt 12KB
  389. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.vtt 12KB
  390. 21. K-Means Clustering/2. K-Means Random Initialization Trap.vtt 12KB
  391. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.vtt 12KB
  392. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.vtt 12KB
  393. 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.vtt 11KB
  394. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.vtt 11KB
  395. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.vtt 11KB
  396. 1. Welcome to the course!/6. Installing Python and Anaconda (Mac, Linux & Windows).vtt 11KB
  397. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.vtt 11KB
  398. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.vtt 11KB
  399. 31. Artificial Neural Networks/3. The Activation Function.vtt 11KB
  400. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.vtt 11KB
  401. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.vtt 10KB
  402. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.vtt 10KB
  403. 7. Support Vector Regression (SVR)/2. SVR Intuition.vtt 10KB
  404. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.vtt 10KB
  405. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.vtt 10KB
  406. 31. Artificial Neural Networks/19. ANN in Python - Step 8.vtt 10KB
  407. 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.vtt 9KB
  408. 15. Kernel SVM/2. Mapping to a higher dimension.vtt 9KB
  409. 9. Random Forest Regression/1. Random Forest Regression Intuition.vtt 9KB
  410. 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.vtt 9KB
  411. 31. Artificial Neural Networks/21. ANN in Python - Step 10.vtt 9KB
  412. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.vtt 9KB
  413. 31. Artificial Neural Networks/23. ANN in R - Step 2.vtt 9KB
  414. 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.vtt 9KB
  415. 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.vtt 9KB
  416. 22. Hierarchical Clustering/6. HC in Python - Step 2.vtt 9KB
  417. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).vtt 9KB
  418. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.vtt 8KB
  419. 14. Support Vector Machine (SVM)/4.1 SVM.zip.zip 8KB
  420. 31. Artificial Neural Networks/20. ANN in Python - Step 9.vtt 8KB
  421. 1. Welcome to the course!/8. Installing R and R Studio (Mac, Linux & Windows).vtt 8KB
  422. 1. Welcome to the course!/2. Why Machine Learning is the Future.vtt 8KB
  423. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.vtt 8KB
  424. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.vtt 8KB
  425. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.vtt 8KB
  426. 12. Logistic Regression/9. Logistic Regression in R - Step 1.vtt 8KB
  427. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.vtt 8KB
  428. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.vtt 8KB
  429. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.vtt 8KB
  430. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.vtt 7KB
  431. 22. Hierarchical Clustering/11. HC in R - Step 2.vtt 7KB
  432. 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.vtt 7KB
  433. 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.vtt 7KB
  434. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.vtt 7KB
  435. 25. Eclat/1. Eclat Intuition.vtt 7KB
  436. 6. Polynomial Regression/1. Polynomial Regression Intuition.vtt 7KB
  437. 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.vtt 7KB
  438. 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.vtt 7KB
  439. 22. Hierarchical Clustering/7. HC in Python - Step 3.vtt 7KB
  440. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.vtt 7KB
  441. 22. Hierarchical Clustering/5. HC in Python - Step 1.vtt 7KB
  442. 19. Evaluating Classification Models Performance/2. Confusion Matrix.vtt 7KB
  443. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.vtt 7KB
  444. 12. Logistic Regression/11. Logistic Regression in R - Step 3.vtt 7KB
  445. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.vtt 7KB
  446. 31. Artificial Neural Networks/10. Business Problem Description.vtt 6KB
  447. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.vtt 6KB
  448. 18. Random Forest Classification/1. Random Forest Classification Intuition.vtt 6KB
  449. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.vtt 6KB
  450. 31. Artificial Neural Networks/8. Backpropagation.vtt 6KB
  451. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.vtt 6KB
  452. 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.vtt 6KB
  453. 22. Hierarchical Clustering/9. HC in Python - Step 5.vtt 6KB
  454. 12. Logistic Regression/14. R Classification Template.vtt 6KB
  455. 22. Hierarchical Clustering/8. HC in Python - Step 4.vtt 6KB
  456. 22. Hierarchical Clustering/10. HC in R - Step 1.vtt 6KB
  457. 12. Logistic Regression/8. Python Classification Template.vtt 5KB
  458. 32. Convolutional Neural Networks/8. Summary.vtt 5KB
  459. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.vtt 5KB
  460. 31. Artificial Neural Networks/18. ANN in Python - Step 7.vtt 5KB
  461. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.vtt 5KB
  462. 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.vtt 5KB
  463. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.vtt 5KB
  464. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.vtt 5KB
  465. 40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 5KB
  466. 1. Welcome to the course!/1. Applications of Machine Learning.vtt 5KB
  467. 32. Convolutional Neural Networks/1. Plan of attack.vtt 5KB
  468. 31. Artificial Neural Networks/14. ANN in Python - Step 3.vtt 5KB
  469. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.vtt 5KB
  470. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.vtt 4KB
  471. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.vtt 4KB
  472. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.vtt 4KB
  473. 15. Kernel SVM/4. Types of Kernel Functions.vtt 4KB
  474. 22. Hierarchical Clustering/12. HC in R - Step 3.vtt 4KB
  475. 12. Logistic Regression/2. How to get the dataset.vtt 4KB
  476. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.vtt 4KB
  477. 14. Support Vector Machine (SVM)/2. How to get the dataset.vtt 4KB
  478. 15. Kernel SVM/5. How to get the dataset.vtt 4KB
  479. 16. Naive Bayes/5. How to get the dataset.vtt 4KB
  480. 17. Decision Tree Classification/2. How to get the dataset.vtt 4KB
  481. 18. Random Forest Classification/2. How to get the dataset.vtt 4KB
  482. 21. K-Means Clustering/4. How to get the dataset.vtt 4KB
  483. 22. Hierarchical Clustering/4. How to get the dataset.vtt 4KB
  484. 24. Apriori/2. How to get the dataset.vtt 4KB
  485. 25. Eclat/2. How to get the dataset.vtt 4KB
  486. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.vtt 4KB
  487. 28. Thompson Sampling/3. How to get the dataset.vtt 4KB
  488. 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.vtt 4KB
  489. 31. Artificial Neural Networks/9. How to get the dataset.vtt 4KB
  490. 32. Convolutional Neural Networks/10. How to get the dataset.vtt 4KB
  491. 34. Principal Component Analysis (PCA)/2. How to get the dataset.vtt 4KB
  492. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.vtt 4KB
  493. 36. Kernel PCA/1. How to get the dataset.vtt 4KB
  494. 38. Model Selection/1. How to get the dataset.vtt 4KB
  495. 39. XGBoost/1. How to get the dataset.vtt 4KB
  496. 4. Simple Linear Regression/1. How to get the dataset.vtt 4KB
  497. 5. Multiple Linear Regression/1. How to get the dataset.vtt 4KB
  498. 6. Polynomial Regression/2. How to get the dataset.vtt 4KB
  499. 7. Support Vector Regression (SVR)/1. How to get the dataset.vtt 4KB
  500. 8. Decision Tree Regression/2. How to get the dataset.vtt 4KB
  501. 9. Random Forest Regression/2. How to get the dataset.vtt 4KB
  502. 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.vtt 4KB
  503. 31. Artificial Neural Networks/17. ANN in Python - Step 6.vtt 4KB
  504. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.vtt 4KB
  505. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.vtt 4KB
  506. 12. Logistic Regression/10. Logistic Regression in R - Step 2.vtt 4KB
  507. 15. Kernel SVM/1. Kernel SVM Intuition.vtt 4KB
  508. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.vtt 4KB
  509. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.vtt 4KB
  510. 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.vtt 4KB
  511. 19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 4KB
  512. 4. Simple Linear Regression/2. Dataset + Business Problem Description.vtt 4KB
  513. 1. Welcome to the course!/3. Important notes, tips & tricks for this course.html 4KB
  514. 22. Hierarchical Clustering/14. HC in R - Step 5.vtt 4KB
  515. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.vtt 4KB
  516. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.vtt 4KB
  517. 1. Welcome to the course!/5. Updates on Udemy Reviews.vtt 4KB
  518. 12. Logistic Regression/12. Logistic Regression in R - Step 4.vtt 4KB
  519. 31. Artificial Neural Networks/1. Plan of attack.vtt 4KB
  520. 22. Hierarchical Clustering/13. HC in R - Step 4.vtt 3KB
  521. 31. Artificial Neural Networks/15. ANN in Python - Step 4.vtt 3KB
  522. 10. Evaluating Regression Models Performance/5. Conclusion of Part 2 - Regression.html 3KB
  523. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.vtt 3KB
  524. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.vtt 3KB
  525. 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.vtt 3KB
  526. 32. Convolutional Neural Networks/22. CNN in R.html 2KB
  527. 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.vtt 2KB
  528. 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.vtt 2KB
  529. 32. Convolutional Neural Networks/6. Step 3 - Flattening.vtt 2KB
  530. 5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html 2KB
  531. 29. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html 2KB
  532. 2. -------------------- Part 1 Data Preprocessing --------------------/5. For Python learners, summary of Object-oriented programming classes & objects.html 2KB
  533. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.vtt 2KB
  534. 1. Welcome to the course!/4. This PDF resource will help you a lot.html 1KB
  535. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.vtt 1KB
  536. 31. Artificial Neural Networks/11. Installing Keras.html 1KB
  537. 29. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html 1KB
  538. 29. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1KB
  539. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.vtt 1KB
  540. 1. Welcome to the course!/7. Update Recommended Anaconda Version.html 1KB
  541. 33. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1KB
  542. 1. Welcome to the course!/9. BONUS Meet your instructors.html 1KB
  543. 32. Convolutional Neural Networks/11. Installing Keras.html 927B
  544. 37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899B
  545. 3. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875B
  546. 30. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870B
  547. 11. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html 831B
  548. 26. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 804B
  549. 2. -------------------- Part 1 Data Preprocessing --------------------/8. WARNING - Update.html 783B
  550. 20. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734B
  551. 5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html 726B
  552. 5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676B
  553. 22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 506B
  554. 23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425B
  555. [FCS Forum].url 133B
  556. [FreeCourseSite.com].url 127B
  557. [CourseClub.NET].url 123B
  558. 12. Logistic Regression/15. Logistic Regression.html 121B
  559. 13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 121B
  560. 2. -------------------- Part 1 Data Preprocessing --------------------/12. Data Preprocessing.html 121B
  561. 21. K-Means Clustering/7. K-Means Clustering.html 121B
  562. 22. Hierarchical Clustering/15. Hierarchical Clustering.html 121B
  563. 4. Simple Linear Regression/13. Simple Linear Regression.html 121B
  564. 5. Multiple Linear Regression/22. Multiple Linear Regression.html 121B