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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:22
  • 最近下载:2021-01-23 15:41:24
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文件列表

  1. 1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip 228MB
  2. 36. Kernel PCA/3. Kernel PCA in R.mp4 57MB
  3. 1. Welcome to the course!/7. Updates on Udemy Reviews.mp4 53MB
  4. 39. XGBoost/5. THANK YOU bonus video.mp4 52MB
  5. 12. Logistic Regression/14. Logistic Regression in R - Step 5.mp4 52MB
  6. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51MB
  7. 17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51MB
  8. 18. Random Forest Classification/4. Random Forest Classification in R.mp4 49MB
  9. 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48MB
  10. 39. XGBoost/4. XGBoost in R.mp4 47MB
  11. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47MB
  12. 18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47MB
  13. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 47MB
  14. 7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 47MB
  15. 7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46MB
  16. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45MB
  17. 8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44MB
  18. 16. Naive Bayes/1. Bayes Theorem.mp4 44MB
  19. 24. Apriori/5. Apriori in R - Step 3.mp4 44MB
  20. 38. Model Selection/3. k-Fold Cross Validation in R.mp4 44MB
  21. 6. Polynomial Regression/11. Polynomial Regression in R - Step 3.mp4 43MB
  22. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43MB
  23. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 3.mp4 43MB
  24. 24. Apriori/3. Apriori in R - Step 1.mp4 43MB
  25. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43MB
  26. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 43MB
  27. 15. Kernel SVM/6. Kernel SVM in Python.mp4 42MB
  28. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41MB
  29. 29. Part 7 Natural Language Processing/24. Natural Language Processing in R - Step 10.mp4 41MB
  30. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41MB
  31. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 41MB
  32. 2. Part 1 Data Preprocessing/7. Categorical Data.mp4 41MB
  33. 15. Kernel SVM/7. Kernel SVM in R.mp4 40MB
  34. 29. Part 7 Natural Language Processing/15. Natural Language Processing in R - Step 1.mp4 40MB
  35. 9. Random Forest Regression/4. Random Forest Regression in R.mp4 40MB
  36. 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40MB
  37. 21. K-Means Clustering/5. K-Means Clustering in Python.mp4 40MB
  38. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40MB
  39. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 40MB
  40. 29. Part 7 Natural Language Processing/11. Natural Language Processing in Python - Step 8.mp4 39MB
  41. 9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39MB
  42. 2. Part 1 Data Preprocessing/9. Splitting the Dataset into the Training set and Test set.mp4 39MB
  43. 31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 39MB
  44. 38. Model Selection/4. Grid Search in Python - Step 1.mp4 38MB
  45. 24. Apriori/6. Apriori in Python - Step 1.mp4 38MB
  46. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37MB
  47. 16. Naive Bayes/7. Naive Bayes in R.mp4 37MB
  48. 28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37MB
  49. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 37MB
  50. 38. Model Selection/6. Grid Search in R.mp4 36MB
  51. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35MB
  52. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35MB
  53. 29. Part 7 Natural Language Processing/4. Natural Language Processing in Python - Step 1.mp4 35MB
  54. 24. Apriori/1. Apriori Intuition.mp4 35MB
  55. 2. Part 1 Data Preprocessing/10. Feature Scaling.mp4 35MB
  56. 8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 34MB
  57. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33MB
  58. 36. Kernel PCA/2. Kernel PCA in Python.mp4 33MB
  59. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33MB
  60. 38. Model Selection/2. k-Fold Cross Validation in Python.mp4 33MB
  61. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 33MB
  62. 14. Support Vector Machine (SVM)/4. SVM in R.mp4 32MB
  63. 2. Part 1 Data Preprocessing/6. Missing Data.mp4 32MB
  64. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32MB
  65. 39. XGBoost/3. XGBoost in Python - Step 2.mp4 32MB
  66. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 32MB
  67. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 32MB
  68. 30. Part 8 Deep Learning/2. What is Deep Learning.mp4 31MB
  69. 14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31MB
  70. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31MB
  71. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 31MB
  72. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 31MB
  73. 24. Apriori/4. Apriori in R - Step 2.mp4 30MB
  74. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30MB
  75. 31. Artificial Neural Networks/2. The Neuron.mp4 30MB
  76. 17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 30MB
  77. 29. Part 7 Natural Language Processing/2. Natural Language Processing Intuition.mp4 30MB
  78. 31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 30MB
  79. 24. Apriori/7. Apriori in Python - Step 2.mp4 30MB
  80. 38. Model Selection/5. Grid Search in Python - Step 2.mp4 30MB
  81. 32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29MB
  82. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29MB
  83. 31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29MB
  84. 15. Kernel SVM/3. The Kernel Trick.mp4 29MB
  85. 12. Logistic Regression/1. Logistic Regression Intuition.mp4 29MB
  86. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29MB
  87. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29MB
  88. 21. K-Means Clustering/6. K-Means Clustering in R.mp4 29MB
  89. 29. Part 7 Natural Language Processing/23. Natural Language Processing in R - Step 9.mp4 29MB
  90. 31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 29MB
  91. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 29MB
  92. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28MB
  93. 16. Naive Bayes/2. Naive Bayes Intuition.mp4 28MB
  94. 6. Polynomial Regression/8. Python Regression Template.mp4 27MB
  95. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27MB
  96. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27MB
  97. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 2.mp4 27MB
  98. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
  99. 24. Apriori/8. Apriori in Python - Step 3.mp4 27MB
  100. 21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 27MB
  101. 31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27MB
  102. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 26MB
  103. 7. Support Vector Regression (SVR)/4. SVR in R.mp4 26MB
  104. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 26MB
  105. 6. Polynomial Regression/13. R Regression Template.mp4 25MB
  106. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 25MB
  107. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 1.mp4 25MB
  108. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24MB
  109. 29. Part 7 Natural Language Processing/13. Natural Language Processing in Python - Step 10.mp4 24MB
  110. 29. Part 7 Natural Language Processing/7. Natural Language Processing in Python - Step 4.mp4 24MB
  111. 6. Polynomial Regression/10. Polynomial Regression in R - Step 2.mp4 24MB
  112. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 24MB
  113. 31. Artificial Neural Networks/4. How do Neural Networks work.mp4 24MB
  114. 16. Naive Bayes/6. Naive Bayes in Python.mp4 23MB
  115. 2. Part 1 Data Preprocessing/4. Importing the Dataset.mp4 23MB
  116. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23MB
  117. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 23MB
  118. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 23MB
  119. 6. Polynomial Regression/12. Polynomial Regression in R - Step 4.mp4 22MB
  120. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22MB
  121. 29. Part 7 Natural Language Processing/5. Natural Language Processing in Python - Step 2.mp4 22MB
  122. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 22MB
  123. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 22MB
  124. 39. XGBoost/2. XGBoost in Python - Step 1.mp4 21MB
  125. 2. Part 1 Data Preprocessing/2. Get the dataset.mp4 21MB
  126. 25. Eclat/3. Eclat in R.mp4 21MB
  127. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 21MB
  128. 2. Part 1 Data Preprocessing/11. And here is our Data Preprocessing Template!.mp4 20MB
  129. 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp4 20MB
  130. 18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19MB
  131. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19MB
  132. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19MB
  133. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 19MB
  134. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 19MB
  135. 19. Evaluating Classification Models Performance/4. CAP Curve.mp4 19MB
  136. 31. Artificial Neural Networks/6. Gradient Descent.mp4 19MB
  137. 31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18MB
  138. 14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18MB
  139. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18MB
  140. 6. Polynomial Regression/9. Polynomial Regression in R - Step 1.mp4 18MB
  141. 1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 18MB
  142. 29. Part 7 Natural Language Processing/16. Natural Language Processing in R - Step 2.mp4 17MB
  143. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17MB
  144. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17MB
  145. 29. Part 7 Natural Language Processing/10. Natural Language Processing in Python - Step 7.mp4 17MB
  146. 31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17MB
  147. 31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 17MB
  148. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17MB
  149. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 17MB
  150. 31. Artificial Neural Networks/10. Business Problem Description.mp4 16MB
  151. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 16MB
  152. 21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15MB
  153. 29. Part 7 Natural Language Processing/8. Natural Language Processing in Python - Step 5.mp4 15MB
  154. 31. Artificial Neural Networks/3. The Activation Function.mp4 15MB
  155. 12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 15MB
  156. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14MB
  157. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14MB
  158. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14MB
  159. 31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14MB
  160. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14MB
  161. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
  162. 29. Part 7 Natural Language Processing/12. Natural Language Processing in Python - Step 9.mp4 14MB
  163. 9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 14MB
  164. 15. Kernel SVM/2. Mapping to a higher dimension.mp4 14MB
  165. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 14MB
  166. 29. Part 7 Natural Language Processing/17. Natural Language Processing in R - Step 3.mp4 14MB
  167. 6. Polynomial Regression/7. Polynomial Regression in Python - Step 4.mp4 14MB
  168. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13MB
  169. 29. Part 7 Natural Language Processing/22. Natural Language Processing in R - Step 8.mp4 13MB
  170. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 13MB
  171. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 13MB
  172. 1. Welcome to the course!/3. Why Machine Learning is the Future.mp4 13MB
  173. 29. Part 7 Natural Language Processing/20. Natural Language Processing in R - Step 6.mp4 13MB
  174. 22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 13MB
  175. 12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 13MB
  176. 12. Logistic Regression/15. R Classification Template.mp4 12MB
  177. 15. Kernel SVM/4. Types of Kernel Functions.mp4 12MB
  178. 22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12MB
  179. 12. Logistic Regression/8. Python Classification Template.mp4 12MB
  180. 22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12MB
  181. 16. Naive Bayes/5. How to get the dataset.mp4 12MB
  182. 18. Random Forest Classification/2. How to get the dataset.mp4 12MB
  183. 21. K-Means Clustering/4. How to get the dataset.mp4 12MB
  184. 22. Hierarchical Clustering/4. How to get the dataset.mp4 12MB
  185. 31. Artificial Neural Networks/9. How to get the dataset.mp4 12MB
  186. 32. Convolutional Neural Networks/10. How to get the dataset.mp4 12MB
  187. 38. Model Selection/1. How to get the dataset.mp4 12MB
  188. 4. Simple Linear Regression/1. How to get the dataset.mp4 12MB
  189. 9. Random Forest Regression/2. How to get the dataset.mp4 12MB
  190. 12. Logistic Regression/2. How to get the dataset.mp4 12MB
  191. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 12MB
  192. 14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12MB
  193. 15. Kernel SVM/5. How to get the dataset.mp4 12MB
  194. 17. Decision Tree Classification/2. How to get the dataset.mp4 12MB
  195. 24. Apriori/2. How to get the dataset.mp4 12MB
  196. 25. Eclat/2. How to get the dataset.mp4 12MB
  197. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12MB
  198. 28. Thompson Sampling/3. How to get the dataset.mp4 12MB
  199. 29. Part 7 Natural Language Processing/3. How to get the dataset.mp4 12MB
  200. 34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 12MB
  201. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 12MB
  202. 36. Kernel PCA/1. How to get the dataset.mp4 12MB
  203. 39. XGBoost/1. How to get the dataset.mp4 12MB
  204. 5. Multiple Linear Regression/1. How to get the dataset.mp4 12MB
  205. 6. Polynomial Regression/2. How to get the dataset.mp4 12MB
  206. 7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 12MB
  207. 8. Decision Tree Regression/2. How to get the dataset.mp4 12MB
  208. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12MB
  209. 22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11MB
  210. 2. Part 1 Data Preprocessing/3. Importing the Libraries.mp4 11MB
  211. 31. Artificial Neural Networks/8. Backpropagation.mp4 11MB
  212. 22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 11MB
  213. 25. Eclat/1. Eclat Intuition.mp4 11MB
  214. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10MB
  215. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10MB
  216. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10MB
  217. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 10MB
  218. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 10MB
  219. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 10MB
  220. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9MB
  221. 6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9MB
  222. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9MB
  223. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9MB
  224. 31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9MB
  225. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9MB
  226. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 9MB
  227. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8MB
  228. 22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8MB
  229. 31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8MB
  230. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8MB
  231. 19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8MB
  232. 1. Welcome to the course!/1. Applications of Machine Learning.mp4 8MB
  233. 32. Convolutional Neural Networks/8. Summary.mp4 8MB
  234. 12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 8MB
  235. 22. Hierarchical Clustering/12. HC in R - Step 3.mp4 8MB
  236. 29. Part 7 Natural Language Processing/21. Natural Language Processing in R - Step 7.mp4 8MB
  237. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7MB
  238. 22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7MB
  239. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7MB
  240. 22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7MB
  241. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7MB
  242. 31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7MB
  243. 12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 7MB
  244. 22. Hierarchical Clustering/14. HC in R - Step 5.mp4 7MB
  245. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 7MB
  246. 4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 7MB
  247. 29. Part 7 Natural Language Processing/18. Natural Language Processing in R - Step 4.mp4 6MB
  248. 29. Part 7 Natural Language Processing/9. Natural Language Processing in Python - Step 6.mp4 6MB
  249. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6MB
  250. 32. Convolutional Neural Networks/1. Plan of attack.mp4 6MB
  251. 31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 6MB
  252. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 6MB
  253. 15. Kernel SVM/1. Kernel SVM Intuition.mp4 6MB
  254. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5MB
  255. 31. Artificial Neural Networks/1. Plan of attack.mp4 5MB
  256. 29. Part 7 Natural Language Processing/19. Natural Language Processing in R - Step 5.mp4 5MB
  257. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 5MB
  258. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4MB
  259. 29. Part 7 Natural Language Processing/6. Natural Language Processing in Python - Step 3.mp4 3MB
  260. 32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3MB
  261. 2. Part 1 Data Preprocessing/1. Welcome to Part 1 - Data Preprocessing.mp4 3MB
  262. 1. Welcome to the course!/5.1 Machine_Learning_A_Z_Q_A.pdf 2MB
  263. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2MB
  264. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 2MB
  265. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 2MB
  266. 25. Eclat/3.1 Eclat.zip 49KB
  267. 16. Naive Bayes/1. Bayes Theorem.srt 34KB
  268. 18. Random Forest Classification/4. Random Forest Classification in R.srt 32KB
  269. 8. Decision Tree Regression/4. Decision Tree Regression in R.srt 32KB
  270. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 3.srt 31KB
  271. 24. Apriori/5. Apriori in R - Step 3.srt 31KB
  272. 24. Apriori/3. Apriori in R - Step 1.srt 31KB
  273. 7. Support Vector Regression (SVR)/3. SVR in Python.srt 31KB
  274. 6. Polynomial Regression/11. Polynomial Regression in R - Step 3.srt 31KB
  275. 36. Kernel PCA/3. Kernel PCA in R.srt 31KB
  276. 18. Random Forest Classification/3. Random Forest Classification in Python.srt 31KB
  277. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.srt 30KB
  278. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 30KB
  279. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.srt 29KB
  280. 17. Decision Tree Classification/4. Decision Tree Classification in R.srt 29KB
  281. 12. Logistic Regression/14. Logistic Regression in R - Step 5.srt 29KB
  282. 31. Artificial Neural Networks/13. ANN in Python - Step 2.srt 29KB
  283. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt 29KB
  284. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 29KB
  285. 15. Kernel SVM/6. Kernel SVM in Python.srt 28KB
  286. 21. K-Means Clustering/5. K-Means Clustering in Python.srt 28KB
  287. 9. Random Forest Regression/4. Random Forest Regression in R.srt 28KB
  288. 24. Apriori/6. Apriori in Python - Step 1.srt 28KB
  289. 38. Model Selection/3. k-Fold Cross Validation in R.srt 28KB
  290. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.srt 28KB
  291. 9. Random Forest Regression/3. Random Forest Regression in Python.srt 28KB
  292. 28. Thompson Sampling/1. Thompson Sampling Intuition.srt 28KB
  293. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt 27KB
  294. 2. Part 1 Data Preprocessing/7. Categorical Data.srt 27KB
  295. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt 27KB
  296. 2. Part 1 Data Preprocessing/9. Splitting the Dataset into the Training set and Test set.srt 27KB
  297. 31. Artificial Neural Networks/22. ANN in R - Step 1.srt 27KB
  298. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt 27KB
  299. 29. Part 7 Natural Language Processing/24. Natural Language Processing in R - Step 10.srt 26KB
  300. 39. XGBoost/4. XGBoost in R.srt 26KB
  301. 24. Apriori/1. Apriori Intuition.srt 26KB
  302. 22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf 26KB
  303. 15. Kernel SVM/7. Kernel SVM in R.srt 25KB
  304. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.srt 25KB
  305. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt 25KB
  306. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt 25KB
  307. 31. Artificial Neural Networks/2. The Neuron.srt 25KB
  308. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt 24KB
  309. 29. Part 7 Natural Language Processing/15. Natural Language Processing in R - Step 1.srt 24KB
  310. 12. Logistic Regression/1. Logistic Regression Intuition.srt 24KB
  311. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.srt 24KB
  312. 29. Part 7 Natural Language Processing/11. Natural Language Processing in Python - Step 8.srt 24KB
  313. 8. Decision Tree Regression/3. Decision Tree Regression in Python.srt 24KB
  314. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.srt 24KB
  315. 2. Part 1 Data Preprocessing/10. Feature Scaling.srt 23KB
  316. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.srt 23KB
  317. 21. K-Means Clustering/1. K-Means Clustering Intuition.srt 23KB
  318. 16. Naive Bayes/2. Naive Bayes Intuition.srt 23KB
  319. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.srt 23KB
  320. 24. Apriori/4. Apriori in R - Step 2.srt 23KB
  321. 2. Part 1 Data Preprocessing/6. Missing Data.srt 23KB
  322. 24. Apriori/7. Apriori in Python - Step 2.srt 23KB
  323. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.srt 22KB
  324. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.srt 22KB
  325. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.srt 22KB
  326. 32. Convolutional Neural Networks/2. What are convolutional neural networks.srt 22KB
  327. 38. Model Selection/4. Grid Search in Python - Step 1.srt 22KB
  328. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.srt 22KB
  329. 16. Naive Bayes/7. Naive Bayes in R.srt 22KB
  330. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.srt 22KB
  331. 36. Kernel PCA/2. Kernel PCA in Python.srt 21KB
  332. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 21KB
  333. 32. Convolutional Neural Networks/5. Step 2 - Pooling.srt 21KB
  334. 38. Model Selection/6. Grid Search in R.srt 21KB
  335. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).srt 21KB
  336. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.srt 21KB
  337. 38. Model Selection/2. k-Fold Cross Validation in Python.srt 20KB
  338. 31. Artificial Neural Networks/12. ANN in Python - Step 1.srt 20KB
  339. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.srt 20KB
  340. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.srt 20KB
  341. 29. Part 7 Natural Language Processing/23. Natural Language Processing in R - Step 9.srt 20KB
  342. 24. Apriori/8. Apriori in Python - Step 3.srt 20KB
  343. 31. Artificial Neural Networks/16. ANN in Python - Step 5.srt 19KB
  344. 21. K-Means Clustering/6. K-Means Clustering in R.srt 19KB
  345. 17. Decision Tree Classification/3. Decision Tree Classification in Python.srt 19KB
  346. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.srt 19KB
  347. 14. Support Vector Machine (SVM)/3. SVM in Python.srt 19KB
  348. 31. Artificial Neural Networks/4. How do Neural Networks work.srt 19KB
  349. 31. Artificial Neural Networks/5. How do Neural Networks learn.srt 19KB
  350. 39. XGBoost/3. XGBoost in Python - Step 2.srt 19KB
  351. 31. Artificial Neural Networks/24. ANN in R - Step 3.srt 19KB
  352. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.srt 19KB
  353. 7. Support Vector Regression (SVR)/4. SVR in R.srt 19KB
  354. 2. Part 1 Data Preprocessing/4. Importing the Dataset.srt 19KB
  355. 6. Polynomial Regression/13. R Regression Template.srt 19KB
  356. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.srt 18KB
  357. 14. Support Vector Machine (SVM)/4. SVM in R.srt 18KB
  358. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.srt 18KB
  359. 29. Part 7 Natural Language Processing/4. Natural Language Processing in Python - Step 1.srt 18KB
  360. 30. Part 8 Deep Learning/2. What is Deep Learning.srt 18KB
  361. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.srt 18KB
  362. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.srt 18KB
  363. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 1.srt 17KB
  364. 29. Part 7 Natural Language Processing/7. Natural Language Processing in Python - Step 4.srt 17KB
  365. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 2.srt 17KB
  366. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.srt 17KB
  367. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.srt 17KB
  368. 15. Kernel SVM/3. The Kernel Trick.srt 17KB
  369. 6. Polynomial Regression/8. Python Regression Template.srt 16KB
  370. 19. Evaluating Classification Models Performance/4. CAP Curve.srt 16KB
  371. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).srt 16KB
  372. 29. Part 7 Natural Language Processing/5. Natural Language Processing in Python - Step 2.srt 16KB
  373. 25. Eclat/3. Eclat in R.srt 16KB
  374. 14. Support Vector Machine (SVM)/1. SVM Intuition.srt 16KB
  375. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.srt 15KB
  376. 6. Polynomial Regression/12. Polynomial Regression in R - Step 4.srt 15KB
  377. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.srt 15KB
  378. 38. Model Selection/5. Grid Search in Python - Step 2.srt 15KB
  379. 6. Polynomial Regression/10. Polynomial Regression in R - Step 2.srt 15KB
  380. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.srt 15KB
  381. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.srt 15KB
  382. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.srt 15KB
  383. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.srt 14KB
  384. 29. Part 7 Natural Language Processing/13. Natural Language Processing in Python - Step 10.srt 14KB
  385. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.srt 14KB
  386. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.srt 14KB
  387. 2. Part 1 Data Preprocessing/11. And here is our Data Preprocessing Template!.srt 14KB
  388. 6. Polynomial Regression/9. Polynomial Regression in R - Step 1.srt 14KB
  389. 31. Artificial Neural Networks/6. Gradient Descent.srt 14KB
  390. 16. Naive Bayes/6. Naive Bayes in Python.srt 14KB
  391. 39. XGBoost/2. XGBoost in Python - Step 1.srt 14KB
  392. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.srt 13KB
  393. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.srt 13KB
  394. 21. K-Means Clustering/2. K-Means Random Initialization Trap.srt 13KB
  395. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.srt 13KB
  396. 29. Part 7 Natural Language Processing/16. Natural Language Processing in R - Step 2.srt 13KB
  397. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.srt 13KB
  398. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.srt 12KB
  399. 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).srt 12KB
  400. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.srt 12KB
  401. 31. Artificial Neural Networks/3. The Activation Function.srt 12KB
  402. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt 12KB
  403. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.srt 12KB
  404. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.srt 12KB
  405. 7. Support Vector Regression (SVR)/2. SVR Intuition.srt 11KB
  406. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.srt 11KB
  407. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.srt 11KB
  408. 31. Artificial Neural Networks/19. ANN in Python - Step 8.srt 11KB
  409. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.srt 11KB
  410. 2. Part 1 Data Preprocessing/2. Get the dataset.srt 11KB
  411. 29. Part 7 Natural Language Processing/8. Natural Language Processing in Python - Step 5.srt 11KB
  412. 15. Kernel SVM/2. Mapping to a higher dimension.srt 11KB
  413. 31. Artificial Neural Networks/21. ANN in Python - Step 10.srt 10KB
  414. 9. Random Forest Regression/1. Random Forest Regression Intuition.srt 10KB
  415. 29. Part 7 Natural Language Processing/17. Natural Language Processing in R - Step 3.srt 10KB
  416. 31. Artificial Neural Networks/23. ANN in R - Step 2.srt 10KB
  417. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.srt 10KB
  418. 29. Part 7 Natural Language Processing/10. Natural Language Processing in Python - Step 7.srt 10KB
  419. 22. Hierarchical Clustering/6. HC in Python - Step 2.srt 10KB
  420. 31. Artificial Neural Networks/20. ANN in Python - Step 9.srt 10KB
  421. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).srt 9KB
  422. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt 9KB
  423. 1. Welcome to the course!/3. Why Machine Learning is the Future.srt 9KB
  424. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.srt 9KB
  425. 1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).srt 9KB
  426. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.srt 9KB
  427. 12. Logistic Regression/9. Logistic Regression in R - Step 1.srt 9KB
  428. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.srt 9KB
  429. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.srt 9KB
  430. 6. Polynomial Regression/7. Polynomial Regression in Python - Step 4.srt 9KB
  431. 29. Part 7 Natural Language Processing/20. Natural Language Processing in R - Step 6.srt 8KB
  432. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.srt 8KB
  433. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.srt 8KB
  434. 14. Support Vector Machine (SVM)/4.1 SVM.zip 8KB
  435. 29. Part 7 Natural Language Processing/12. Natural Language Processing in Python - Step 9.srt 8KB
  436. 22. Hierarchical Clustering/11. HC in R - Step 2.srt 8KB
  437. 25. Eclat/1. Eclat Intuition.srt 8KB
  438. 2. Part 1 Data Preprocessing/3. Importing the Libraries.srt 8KB
  439. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.srt 8KB
  440. 29. Part 7 Natural Language Processing/22. Natural Language Processing in R - Step 8.srt 8KB
  441. 6. Polynomial Regression/1. Polynomial Regression Intuition.srt 8KB
  442. 22. Hierarchical Clustering/7. HC in Python - Step 3.srt 8KB
  443. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.srt 8KB
  444. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.srt 8KB
  445. 22. Hierarchical Clustering/5. HC in Python - Step 1.srt 8KB
  446. 19. Evaluating Classification Models Performance/2. Confusion Matrix.srt 8KB
  447. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.srt 7KB
  448. 12. Logistic Regression/11. Logistic Regression in R - Step 3.srt 7KB
  449. 31. Artificial Neural Networks/10. Business Problem Description.srt 7KB
  450. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.srt 7KB
  451. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.srt 7KB
  452. 31. Artificial Neural Networks/8. Backpropagation.srt 7KB
  453. 29. Part 7 Natural Language Processing/2. Natural Language Processing Intuition.srt 7KB
  454. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.srt 7KB
  455. 18. Random Forest Classification/1. Random Forest Classification Intuition.srt 7KB
  456. 22. Hierarchical Clustering/9. HC in Python - Step 5.srt 7KB
  457. 12. Logistic Regression/15. R Classification Template.srt 7KB
  458. 22. Hierarchical Clustering/8. HC in Python - Step 4.srt 6KB
  459. 22. Hierarchical Clustering/10. HC in R - Step 1.srt 6KB
  460. 12. Logistic Regression/8. Python Classification Template.srt 6KB
  461. 32. Convolutional Neural Networks/8. Summary.srt 6KB
  462. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.srt 6KB
  463. 31. Artificial Neural Networks/18. ANN in Python - Step 7.srt 6KB
  464. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.srt 6KB
  465. 29. Part 7 Natural Language Processing/21. Natural Language Processing in R - Step 7.srt 6KB
  466. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.srt 6KB
  467. 1. Welcome to the course!/1. Applications of Machine Learning.srt 5KB
  468. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.srt 5KB
  469. 32. Convolutional Neural Networks/1. Plan of attack.srt 5KB
  470. 31. Artificial Neural Networks/14. ANN in Python - Step 3.srt 5KB
  471. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt 5KB
  472. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt 5KB
  473. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt 5KB
  474. 15. Kernel SVM/4. Types of Kernel Functions.srt 5KB
  475. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.srt 5KB
  476. 12. Logistic Regression/2. How to get the dataset.srt 5KB
  477. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.srt 5KB
  478. 14. Support Vector Machine (SVM)/2. How to get the dataset.srt 5KB
  479. 15. Kernel SVM/5. How to get the dataset.srt 5KB
  480. 16. Naive Bayes/5. How to get the dataset.srt 5KB
  481. 17. Decision Tree Classification/2. How to get the dataset.srt 5KB
  482. 18. Random Forest Classification/2. How to get the dataset.srt 5KB
  483. 21. K-Means Clustering/4. How to get the dataset.srt 5KB
  484. 22. Hierarchical Clustering/4. How to get the dataset.srt 5KB
  485. 24. Apriori/2. How to get the dataset.srt 5KB
  486. 25. Eclat/2. How to get the dataset.srt 5KB
  487. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.srt 5KB
  488. 28. Thompson Sampling/3. How to get the dataset.srt 5KB
  489. 29. Part 7 Natural Language Processing/3. How to get the dataset.srt 5KB
  490. 31. Artificial Neural Networks/9. How to get the dataset.srt 5KB
  491. 32. Convolutional Neural Networks/10. How to get the dataset.srt 5KB
  492. 34. Principal Component Analysis (PCA)/2. How to get the dataset.srt 5KB
  493. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.srt 5KB
  494. 36. Kernel PCA/1. How to get the dataset.srt 5KB
  495. 38. Model Selection/1. How to get the dataset.srt 5KB
  496. 39. XGBoost/1. How to get the dataset.srt 5KB
  497. 4. Simple Linear Regression/1. How to get the dataset.srt 5KB
  498. 5. Multiple Linear Regression/1. How to get the dataset.srt 5KB
  499. 6. Polynomial Regression/2. How to get the dataset.srt 5KB
  500. 7. Support Vector Regression (SVR)/1. How to get the dataset.srt 5KB
  501. 8. Decision Tree Regression/2. How to get the dataset.srt 5KB
  502. 9. Random Forest Regression/2. How to get the dataset.srt 5KB
  503. 22. Hierarchical Clustering/12. HC in R - Step 3.srt 5KB
  504. 40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 5KB
  505. 29. Part 7 Natural Language Processing/18. Natural Language Processing in R - Step 4.srt 5KB
  506. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.srt 5KB
  507. 31. Artificial Neural Networks/17. ANN in Python - Step 6.srt 4KB
  508. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.srt 4KB
  509. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.srt 4KB
  510. 15. Kernel SVM/1. Kernel SVM Intuition.srt 4KB
  511. 29. Part 7 Natural Language Processing/9. Natural Language Processing in Python - Step 6.srt 4KB
  512. 12. Logistic Regression/10. Logistic Regression in R - Step 2.srt 4KB
  513. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.srt 4KB
  514. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.srt 4KB
  515. 4. Simple Linear Regression/2. Dataset + Business Problem Description.srt 4KB
  516. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt 4KB
  517. 1. Welcome to the course!/7. Updates on Udemy Reviews.srt 4KB
  518. 22. Hierarchical Clustering/14. HC in R - Step 5.srt 4KB
  519. 31. Artificial Neural Networks/1. Plan of attack.srt 4KB
  520. 12. Logistic Regression/12. Logistic Regression in R - Step 4.srt 4KB
  521. 31. Artificial Neural Networks/15. ANN in Python - Step 4.srt 4KB
  522. 22. Hierarchical Clustering/13. HC in R - Step 4.srt 4KB
  523. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.srt 4KB
  524. 19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 3KB
  525. 29. Part 7 Natural Language Processing/19. Natural Language Processing in R - Step 5.srt 3KB
  526. 1. Welcome to the course!/4. Important notes, tips & tricks for this course.html 3KB
  527. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.srt 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/6. Natural Language Processing in Python - Step 3.srt 3KB
  531. 32. Convolutional Neural Networks/6. Step 3 - Flattening.srt 3KB
  532. 2. Part 1 Data Preprocessing/1. Welcome to Part 1 - Data Preprocessing.srt 3KB
  533. 32. Convolutional Neural Networks/22. CNN in R.html 2KB
  534. 1. Welcome to the course!/2. BONUS Learning Paths.html 2KB
  535. 39. XGBoost/5. THANK YOU bonus video.srt 2KB
  536. 5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html 2KB
  537. 6. Polynomial Regression/3.1 polynomial_regression-updated.py 2KB
  538. 2. Part 1 Data Preprocessing/5. For Python learners, summary of Object-oriented programming classes & objects.html 2KB
  539. 1. Welcome to the course!/13. FAQBot!.html 2KB
  540. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.srt 2KB
  541. 29. Part 7 Natural Language Processing/1. Welcome to Part 7 - Natural Language Processing.html 2KB
  542. 2. Part 1 Data Preprocessing/7.1 categorical_data.py 2KB
  543. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.srt 2KB
  544. 1. Welcome to the course!/5. This PDF resource will help you a lot.html 1KB
  545. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.srt 1KB
  546. 31. Artificial Neural Networks/11. Installing Keras.html 1KB
  547. 29. Part 7 Natural Language Processing/25. Homework Challenge.html 1KB
  548. 29. Part 7 Natural Language Processing/14. Homework Challenge.html 1KB
  549. 12. Logistic Regression/13. Warning - Update.html 1KB
  550. 1. Welcome to the course!/9. Update Recommended Anaconda Version.html 1KB
  551. 33. Part 9 Dimensionality Reduction/1. Welcome to Part 9 - Dimensionality Reduction.html 1KB
  552. 26. Part 6 Reinforcement Learning/1. Welcome to Part 6 - Reinforcement Learning.html 1KB
  553. 1. Welcome to the course!/11. BONUS Meet your instructors.html 1KB
  554. 1. Welcome to the course!/6. The whole code folder of the course.html 1KB
  555. 2. Part 1 Data Preprocessing/6.1 missing_data.py 976B
  556. 32. Convolutional Neural Networks/11. Installing Keras.html 927B
  557. 37. Part 10 Model Selection & Boosting/1. Welcome to Part 10 - Model Selection & Boosting.html 899B
  558. 3. Part 2 Regression/1. Welcome to Part 2 - Regression.html 875B
  559. 30. Part 8 Deep Learning/1. Welcome to Part 8 - Deep Learning.html 870B
  560. 11. Part 3 Classification/1. Welcome to Part 3 - Classification.html 831B
  561. 20. Part 4 Clustering/1. Welcome to Part 4 - Clustering.html 734B
  562. 5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html 726B
  563. 5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676B
  564. 6. Polynomial Regression/3. Polynomial Regression update for Python.html 609B
  565. 1. Welcome to the course!/12. Some Additional Resources.html 551B
  566. 22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 516B
  567. 23. Part 5 Association Rule Learning/1. Welcome to Part 5 - Association Rule Learning.html 425B
  568. 12. Logistic Regression/16. Logistic Regression.html 125B
  569. 13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 125B
  570. 2. Part 1 Data Preprocessing/12. Data Preprocessing.html 125B
  571. 21. K-Means Clustering/7. K-Means Clustering.html 125B
  572. 22. Hierarchical Clustering/15. Hierarchical Clustering.html 125B
  573. 4. Simple Linear Regression/13. Simple Linear Regression.html 125B
  574. 5. Multiple Linear Regression/22. Multiple Linear Regression.html 125B
  575. [GigaCourse.com].url 49B