• Урок 1. 00:03:23
    Applications of Machine Learning
  • Урок 2. 00:06:39
    Why Machine Learning is the Future
  • Урок 3. 00:16:49
    Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder
  • Урок 4. 00:05:41
    Installing R and R Studio (Mac, Linux & Windows)
  • Урок 5. 00:10:51
    Getting Started
  • Урок 6. 00:03:35
    Importing the Libraries
  • Урок 7. 00:15:43
    Importing the Dataset
  • Урок 8. 00:12:16
    Taking care of Missing Data
  • Урок 9. 00:14:59
    Encoding Categorical Data
  • Урок 10. 00:13:48
    Splitting the dataset into the Training set and Test set
  • Урок 11. 00:20:32
    Feature Scaling
  • Урок 12. 00:01:36
    Getting Started
  • Урок 13. 00:01:58
    Dataset Description
  • Урок 14. 00:02:45
    Importing the Dataset
  • Урок 15. 00:06:23
    Taking care of Missing Data
  • Урок 16. 00:06:03
    Encoding Categorical Data
  • Урок 17. 00:09:35
    Splitting the dataset into the Training set and Test set
  • Урок 18. 00:09:15
    Feature Scaling
  • Урок 19. 00:05:16
    Data Preprocessing Template
  • Урок 20. 00:05:46
    Simple Linear Regression Intuition - Step 1
  • Урок 21. 00:03:10
    Simple Linear Regression Intuition - Step 2
  • Урок 22. 00:12:49
    Simple Linear Regression in Python - Step 1
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    Simple Linear Regression in Python - Step 2
  • Урок 24. 00:04:36
    Simple Linear Regression in Python - Step 3
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    Simple Linear Regression in Python - Step 4
  • Урок 26. 00:04:41
    Simple Linear Regression in R - Step 1
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    Simple Linear Regression in R - Step 2
  • Урок 28. 00:03:40
    Simple Linear Regression in R - Step 3
  • Урок 29. 00:15:57
    Simple Linear Regression in R - Step 4
  • Урок 30. 00:03:45
    Dataset + Business Problem Description
  • Урок 31. 00:01:04
    Multiple Linear Regression Intuition - Step 1
  • Урок 32. 00:01:01
    Multiple Linear Regression Intuition - Step 2
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    Multiple Linear Regression Intuition - Step 3
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    Multiple Linear Regression Intuition - Step 4
  • Урок 35. 00:11:45
    Understanding the P-Value
  • Урок 36. 00:15:42
    Multiple Linear Regression Intuition - Step 5
  • Урок 37. 00:08:31
    Multiple Linear Regression in Python - Step 1
  • Урок 38. 00:09:12
    Multiple Linear Regression in Python - Step 2
  • Урок 39. 00:10:38
    Multiple Linear Regression in Python - Step 3
  • Урок 40. 00:12:32
    Multiple Linear Regression in Python - Step 4
  • Урок 41. 00:07:51
    Multiple Linear Regression in R - Step 1
  • Урок 42. 00:10:27
    Multiple Linear Regression in R - Step 2
  • Урок 43. 00:04:28
    Multiple Linear Regression in R - Step 3
  • Урок 44. 00:17:52
    Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
  • Урок 45. 00:07:35
    Multiple Linear Regression in R - Backward Elimination - Homework Solution
  • Урок 46. 00:05:10
    Polynomial Regression Intuition
  • Урок 47. 00:13:31
    Polynomial Regression in Python - Step 1
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    Polynomial Regression in Python - Step 2
  • Урок 49. 00:12:55
    Polynomial Regression in Python - Step 3
  • Урок 50. 00:08:11
    Polynomial Regression in Python - Step 4
  • Урок 51. 00:09:14
    Polynomial Regression in R - Step 1
  • Урок 52. 00:09:59
    Polynomial Regression in R - Step 2
  • Урок 53. 00:19:55
    Polynomial Regression in R - Step 3
  • Урок 54. 00:09:36
    Polynomial Regression in R - Step 4
  • Урок 55. 00:11:59
    R Regression Template
  • Урок 56. 00:08:10
    SVR Intuition (Updated!)
  • Урок 57. 00:03:58
    Heads-up on non-linear SVR
  • Урок 58. 00:09:16
    SVR in Python - Step 1
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    SVR in Python - Step 2
  • Урок 60. 00:06:28
    SVR in Python - Step 3
  • Урок 61. 00:08:02
    SVR in Python - Step 4
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    SVR in Python - Step 5
  • Урок 63. 00:11:45
    SVR in R
  • Урок 64. 00:11:07
    Decision Tree Regression Intuition
  • Урок 65. 00:08:39
    Decision Tree Regression in Python - Step 1
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    Decision Tree Regression in Python - Step 2
  • Урок 67. 00:03:17
    Decision Tree Regression in Python - Step 3
  • Урок 68. 00:09:51
    Decision Tree Regression in Python - Step 4
  • Урок 69. 00:19:55
    Decision Tree Regression in R
  • Урок 70. 00:06:45
    Random Forest Regression Intuition
  • Урок 71. 00:13:24
    Random Forest Regression in Python
  • Урок 72. 00:17:44
    Random Forest Regression in R
  • Урок 73. 00:05:12
    R-Squared Intuition
  • Урок 74. 00:09:58
    Adjusted R-Squared Intuition
  • Урок 75. 00:19:27
    Preparation of the Regression Code Templates
  • Урок 76. 00:09:04
    THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!
  • Урок 77. 00:08:55
    Evaluating Regression Models Performance - Homework's Final Part
  • Урок 78. 00:09:17
    Interpreting Linear Regression Coefficients
  • Урок 79. 00:17:08
    Logistic Regression Intuition
  • Урок 80. 00:09:44
    Logistic Regression in Python - Step 1
  • Урок 81. 00:13:39
    Logistic Regression in Python - Step 2
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    Logistic Regression in Python - Step 3
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    Logistic Regression in Python - Step 4
  • Урок 84. 00:06:16
    Logistic Regression in Python - Step 5
  • Урок 85. 00:09:27
    Logistic Regression in Python - Step 6
  • Урок 86. 00:16:07
    Logistic Regression in Python - Step 7
  • Урок 87. 00:06:00
    Logistic Regression in R - Step 1
  • Урок 88. 00:03:00
    Logistic Regression in R - Step 2
  • Урок 89. 00:05:24
    Logistic Regression in R - Step 3
  • Урок 90. 00:02:49
    Logistic Regression in R - Step 4
  • Урок 91. 00:19:25
    Logistic Regression in R - Step 5
  • Урок 92. 00:04:18
    R Classification Template
  • Урок 93. 00:04:54
    K-Nearest Neighbor Intuition
  • Урок 94. 00:19:59
    K-NN in Python
  • Урок 95. 00:15:48
    K-NN in R
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    SVM Intuition
  • Урок 97. 00:14:53
    SVM in Python
  • Урок 98. 00:12:10
    SVM in R
  • Урок 99. 00:03:18
    Kernel SVM Intuition
  • Урок 100. 00:07:51
    Mapping to a higher dimension
  • Урок 101. 00:12:21
    The Kernel Trick
  • Урок 102. 00:03:48
    Types of Kernel Functions
  • Урок 103. 00:10:56
    Non-Linear Kernel SVR (Advanced)
  • Урок 104. 00:13:04
    Kernel SVM in Python
  • Урок 105. 00:16:35
    Kernel SVM in R
  • Урок 106. 00:20:26
    Bayes Theorem
  • Урок 107. 00:14:04
    Naive Bayes Intuition
  • Урок 108. 00:06:05
    Naive Bayes Intuition (Challenge Reveal)
  • Урок 109. 00:09:43
    Naive Bayes Intuition (Extras)
  • Урок 110. 00:14:20
    Naive Bayes in Python
  • Урок 111. 00:14:54
    Naive Bayes in R
  • Урок 112. 00:08:09
    Decision Tree Classification Intuition
  • Урок 113. 00:14:04
    Decision Tree Classification in Python
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    Decision Tree Classification in R
  • Урок 115. 00:04:29
    Random Forest Classification Intuition
  • Урок 116. 00:13:29
    Random Forest Classification in Python
  • Урок 117. 00:19:57
    Random Forest Classification in R
  • Урок 118. 00:21:01
    THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!
  • Урок 119. 00:07:59
    False Positives & False Negatives
  • Урок 120. 00:04:58
    Confusion Matrix
  • Урок 121. 00:02:13
    Accuracy Paradox
  • Урок 122. 00:11:17
    CAP Curve
  • Урок 123. 00:06:20
    CAP Curve Analysis
  • Урок 124. 00:14:18
    K-Means Clustering Intuition
  • Урок 125. 00:07:49
    K-Means Random Initialization Trap
  • Урок 126. 00:11:52
    K-Means Selecting The Number Of Clusters
  • Урок 127. 00:08:26
    K-Means Clustering in Python - Step 1
  • Урок 128. 00:10:37
    K-Means Clustering in Python - Step 2
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    K-Means Clustering in Python - Step 3
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    K-Means Clustering in Python - Step 4
  • Урок 131. 00:19:36
    K-Means Clustering in Python - Step 5
  • Урок 132. 00:11:48
    K-Means Clustering in R
  • Урок 133. 00:08:49
    Hierarchical Clustering Intuition
  • Урок 134. 00:08:49
    Hierarchical Clustering How Dendrograms Work
  • Урок 135. 00:11:22
    Hierarchical Clustering Using Dendrograms
  • Урок 136. 00:06:57
    Hierarchical Clustering in Python - Step 1
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    Hierarchical Clustering in Python - Step 2
  • Урок 138. 00:12:20
    Hierarchical Clustering in Python - Step 3
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    Hierarchical Clustering in R - Step 1
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    Hierarchical Clustering in R - Step 2
  • Урок 141. 00:03:20
    Hierarchical Clustering in R - Step 3
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    Hierarchical Clustering in R - Step 4
  • Урок 143. 00:02:34
    Hierarchical Clustering in R - Step 5
  • Урок 144. 00:18:14
    Apriori Intuition
  • Урок 145. 00:08:47
    Apriori in Python - Step 1
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    Apriori in Python - Step 2
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    Apriori in Python - Step 3
  • Урок 148. 00:19:42
    Apriori in Python - Step 4
  • Урок 149. 00:19:54
    Apriori in R - Step 1
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    Apriori in R - Step 2
  • Урок 151. 00:19:19
    Apriori in R - Step 3
  • Урок 152. 00:06:06
    Eclat Intuition
  • Урок 153. 00:12:01
    Eclat in Python
  • Урок 154. 00:10:10
    Eclat in R
  • Урок 155. 00:15:37
    The Multi-Armed Bandit Problem
  • Урок 156. 00:14:54
    Upper Confidence Bound (UCB) Intuition
  • Урок 157. 00:12:43
    Upper Confidence Bound in Python - Step 1
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    Upper Confidence Bound in Python - Step 2
  • Урок 159. 00:07:17
    Upper Confidence Bound in Python - Step 3
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    Upper Confidence Bound in Python - Step 4
  • Урок 161. 00:06:13
    Upper Confidence Bound in Python - Step 5
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    Upper Confidence Bound in Python - Step 6
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    Upper Confidence Bound in Python - Step 7
  • Урок 164. 00:13:40
    Upper Confidence Bound in R - Step 1
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    Upper Confidence Bound in R - Step 2
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    Upper Confidence Bound in R - Step 3
  • Урок 167. 00:03:19
    Upper Confidence Bound in R - Step 4
  • Урок 168. 00:19:13
    Thompson Sampling Intuition
  • Урок 169. 00:08:13
    Algorithm Comparison: UCB vs Thompson Sampling
  • Урок 170. 00:05:48
    Thompson Sampling in Python - Step 1
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    Thompson Sampling in Python - Step 2
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    Thompson Sampling in Python - Step 3
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    Thompson Sampling in Python - Step 4
  • Урок 174. 00:19:02
    Thompson Sampling in R - Step 1
  • Урок 175. 00:03:28
    Thompson Sampling in R - Step 2
  • Урок 176. 00:03:03
    NLP Intuition
  • Урок 177. 00:04:12
    Types of Natural Language Processing
  • Урок 178. 00:11:23
    Classical vs Deep Learning Models
  • Урок 179. 00:17:06
    Bag-Of-Words Model
  • Урок 180. 00:07:14
    Natural Language Processing in Python - Step 1
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    Natural Language Processing in Python - Step 2
  • Урок 182. 00:12:55
    Natural Language Processing in Python - Step 3
  • Урок 183. 00:11:01
    Natural Language Processing in Python - Step 4
  • Урок 184. 00:17:25
    Natural Language Processing in Python - Step 5
  • Урок 185. 00:09:53
    Natural Language Processing in Python - Step 6
  • Урок 186. 00:16:36
    Natural Language Processing in R - Step 1
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    Natural Language Processing in R - Step 2
  • Урок 188. 00:06:29
    Natural Language Processing in R - Step 3
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    Natural Language Processing in R - Step 4
  • Урок 190. 00:02:06
    Natural Language Processing in R - Step 5
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    Natural Language Processing in R - Step 6
  • Урок 192. 00:03:28
    Natural Language Processing in R - Step 7
  • Урок 193. 00:05:21
    Natural Language Processing in R - Step 8
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    Natural Language Processing in R - Step 9
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    Natural Language Processing in R - Step 10
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    What is Deep Learning?
  • Урок 197. 00:02:53
    Plan of attack
  • Урок 198. 00:16:26
    The Neuron
  • Урок 199. 00:08:30
    The Activation Function
  • Урок 200. 00:12:49
    How do Neural Networks work?
  • Урок 201. 00:13:00
    How do Neural Networks learn?
  • Урок 202. 00:10:14
    Gradient Descent
  • Урок 203. 00:08:45
    Stochastic Gradient Descent
  • Урок 204. 00:05:23
    Backpropagation
  • Урок 205. 00:05:00
    Business Problem Description
  • Урок 206. 00:10:22
    ANN in Python - Step 1
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    ANN in Python - Step 2
  • Урок 208. 00:14:29
    ANN in Python - Step 3
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    ANN in Python - Step 4
  • Урок 210. 00:16:26
    ANN in Python - Step 5
  • Урок 211. 00:17:18
    ANN in R - Step 1
  • Урок 212. 00:06:31
    ANN in R - Step 2
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    ANN in R - Step 3
  • Урок 214. 00:14:08
    ANN in R - Step 4 (Last step)
  • Урок 215. 00:03:32
    Plan of attack
  • Урок 216. 00:15:50
    What are convolutional neural networks?
  • Урок 217. 00:16:39
    Step 1 - Convolution Operation
  • Урок 218. 00:06:42
    Step 1(b) - ReLU Layer
  • Урок 219. 00:14:14
    Step 2 - Pooling
  • Урок 220. 00:01:53
    Step 3 - Flattening
  • Урок 221. 00:19:26
    Step 4 - Full Connection
  • Урок 222. 00:04:20
    Summary
  • Урок 223. 00:18:21
    Softmax & Cross-Entropy
  • Урок 224. 00:11:36
    CNN in Python - Step 1
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    CNN in Python - Step 2
  • Урок 226. 00:17:57
    CNN in Python - Step 3
  • Урок 227. 00:07:22
    CNN in Python - Step 4
  • Урок 228. 00:14:56
    CNN in Python - Step 5
  • Урок 229. 00:23:39
    CNN in Python - FINAL DEMO!
  • Урок 230. 00:03:50
    Principal Component Analysis (PCA) Intuition
  • Урок 231. 00:16:53
    PCA in Python - Step 1
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    PCA in Python - Step 2
  • Урок 233. 00:12:09
    PCA in R - Step 1
  • Урок 234. 00:11:23
    PCA in R - Step 2
  • Урок 235. 00:13:43
    PCA in R - Step 3
  • Урок 236. 00:03:51
    Linear Discriminant Analysis (LDA) Intuition
  • Урок 237. 00:14:53
    LDA in Python
  • Урок 238. 00:20:01
    LDA in R
  • Урок 239. 00:11:04
    Kernel PCA in Python
  • Урок 240. 00:20:31
    Kernel PCA in R
  • Урок 241. 00:17:56
    k-Fold Cross Validation in Python
  • Урок 242. 00:21:57
    Grid Search in Python
  • Урок 243. 00:19:30
    k-Fold Cross Validation in R
  • Урок 244. 00:14:00
    Grid Search in R
  • Урок 245. 00:14:49
    XGBoost in Python
  • Урок 246. 00:18:15
    XGBoost in R
  • Урок 247. 00:02:41
    THANK YOU Bonus Video
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