Видео курса

  • Урок 1. 00:03:23
    Applications of Machine Learning
  • Урок 2. 00:06:39
    Why Machine Learning is the Future
  • Урок 3. 00:02:39
    Updates on Udemy Reviews
  • Урок 4. 00:07:32
    Installing Python and Anaconda (Mac, Linux & Windows)
  • Урок 5. 00:05:41
    Installing R and R Studio (Mac, Linux & Windows)
  • Урок 6. 00:01:36
    Welcome to Part 1 - Data Preprocessing
  • Урок 7. 00:06:59
    Get the dataset
  • Урок 8. 00:05:21
    Importing the Libraries
  • Урок 9. 00:11:56
    Importing the Dataset
  • Урок 10. 00:15:58
    Missing Data
  • Урок 11. 00:18:02
    Categorical Data
  • Урок 12. 00:17:38
    Splitting the Dataset into the Training set and Test set
  • Урок 13. 00:15:37
    Feature Scaling
  • Урок 14. 00:08:49
    And here is our Data Preprocessing Template!
  • Урок 15. 00:03:20
    How to get the dataset
  • Урок 16. 00:02:57
    Dataset + Business Problem Description
  • Урок 17. 00:05:46
    Simple Linear Regression Intuition - Step 1
  • Урок 18. 00:03:10
    Simple Linear Regression Intuition - Step 2
  • Урок 19. 00:09:57
    Simple Linear Regression in Python - Step 1
  • Урок 20. 00:08:21
    Simple Linear Regression in Python - Step 2
  • Урок 21. 00:06:44
    Simple Linear Regression in Python - Step 3
  • Урок 22. 00:14:51
    Simple Linear Regression in Python - Step 4
  • Урок 23. 00:04:41
    Simple Linear Regression in R - Step 1
  • Урок 24. 00:05:59
    Simple Linear Regression in R - Step 2
  • Урок 25. 00:03:40
    Simple Linear Regression in R - Step 3
  • Урок 26. 00:15:57
    Simple Linear Regression in R - Step 4
  • Урок 27. 00:03:20
    How to get the dataset
  • Урок 28. 00:03:45
    Dataset + Business Problem Description
  • Урок 29. 00:01:04
    Multiple Linear Regression Intuition - Step 1
  • Урок 30. 00:01:01
    Multiple Linear Regression Intuition - Step 2
  • Урок 31. 00:07:22
    Multiple Linear Regression Intuition - Step 3
  • Урок 32. 00:02:11
    Multiple Linear Regression Intuition - Step 4
  • Урок 33. 00:15:42
    Multiple Linear Regression Intuition - Step 5
  • Урок 34. 00:15:58
    Multiple Linear Regression in Python - Step 1
  • Урок 35. 00:02:58
    Multiple Linear Regression in Python - Step 2
  • Урок 36. 00:05:29
    Multiple Linear Regression in Python - Step 3
  • Урок 37. 00:09:59
    Multiple Linear Regression in Python - Backward Elimination - Preparation
  • Урок 38. 00:12:41
    Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
  • Урок 39. 00:09:12
    Multiple Linear Regression in Python - Backward Elimination - Homework Solution
  • Урок 40. 00:07:51
    Multiple Linear Regression in R - Step 1
  • Урок 41. 00:10:27
    Multiple Linear Regression in R - Step 2
  • Урок 42. 00:04:28
    Multiple Linear Regression in R - Step 3
  • Урок 43. 00:17:52
    Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
  • Урок 44. 00:07:35
    Multiple Linear Regression in R - Backward Elimination - Homework Solution
  • Урок 45. 00:05:10
    Polynomial Regression Intuition
  • Урок 46. 00:03:20
    How to get the dataset
  • Урок 47. 00:11:40
    Polynomial Regression in Python - Step 1
  • Урок 48. 00:11:46
    Polynomial Regression in Python - Step 2
  • Урок 49. 00:19:59
    Polynomial Regression in Python - Step 3
  • Урок 50. 00:05:47
    Polynomial Regression in Python - Step 4
  • Урок 51. 00:11:00
    Python Regression Template
  • Урок 52. 00:09:14
    Polynomial Regression in R - Step 1
  • Урок 53. 00:09:59
    Polynomial Regression in R - Step 2
  • Урок 54. 00:19:55
    Polynomial Regression in R - Step 3
  • Урок 55. 00:09:36
    Polynomial Regression in R - Step 4
  • Урок 56. 00:11:59
    R Regression Template
  • Урок 57. 00:03:20
    How to get the dataset
  • Урок 58. 00:08:30
    SVR Intuition
  • Урок 59. 00:19:58
    SVR in Python
  • Урок 60. 00:11:45
    SVR in R
  • Урок 61. 00:11:07
    Decision Tree Regression Intuition
  • Урок 62. 00:03:20
    How to get the dataset
  • Урок 63. 00:14:46
    Decision Tree Regression in Python
  • Урок 64. 00:19:55
    Decision Tree Regression in R
  • Урок 65. 00:06:45
    Random Forest Regression Intuition
  • Урок 66. 00:03:20
    How to get the dataset
  • Урок 67. 00:16:45
    Random Forest Regression in Python
  • Урок 68. 00:17:44
    Random Forest Regression in R
  • Урок 69. 00:05:12
    R-Squared Intuition
  • Урок 70. 00:09:58
    Adjusted R-Squared Intuition
  • Урок 71. 00:08:55
    Evaluating Regression Models Performance - Homework's Final Part
  • Урок 72. 00:09:17
    Interpreting Linear Regression Coefficients
  • Урок 73. 00:17:08
    Logistic Regression Intuition
  • Урок 74. 00:03:20
    How to get the dataset
  • Урок 75. 00:05:48
    Logistic Regression in Python - Step 1
  • Урок 76. 00:03:25
    Logistic Regression in Python - Step 2
  • Урок 77. 00:02:36
    Logistic Regression in Python - Step 3
  • Урок 78. 00:04:34
    Logistic Regression in Python - Step 4
  • Урок 79. 00:19:41
    Logistic Regression in Python - Step 5
  • Урок 80. 00:03:54
    Python Classification Template
  • Урок 81. 00:06:00
    Logistic Regression in R - Step 1
  • Урок 82. 00:03:00
    Logistic Regression in R - Step 2
  • Урок 83. 00:05:24
    Logistic Regression in R - Step 3
  • Урок 84. 00:02:49
    Logistic Regression in R - Step 4
  • Урок 85. 00:19:25
    Logistic Regression in R - Step 5
  • Урок 86. 00:04:18
    R Classification Template
  • Урок 87. 00:04:54
    K-Nearest Neighbor Intuition
  • Урок 88. 00:03:20
    How to get the dataset
  • Урок 89. 00:14:11
    K-NN in Python
  • Урок 90. 00:15:48
    K-NN in R
  • Урок 91. 00:09:50
    SVM Intuition
  • Урок 92. 00:03:20
    How to get the dataset
  • Урок 93. 00:12:25
    SVM in Python
  • Урок 94. 00:12:10
    SVM in R
  • Урок 95. 00:03:18
    Kernel SVM Intuition
  • Урок 96. 00:07:51
    Mapping to a higher dimension
  • Урок 97. 00:12:21
    The Kernel Trick
  • Урок 98. 00:03:48
    Types of Kernel Functions
  • Урок 99. 00:03:20
    How to get the dataset
  • Урок 100. 00:17:53
    Kernel SVM in Python
  • Урок 101. 00:16:35
    Kernel SVM in R
  • Урок 102. 00:20:26
    Bayes Theorem
  • Урок 103. 00:14:04
    Naive Bayes Intuition
  • Урок 104. 00:06:05
    Naive Bayes Intuition (Challenge Reveal)
  • Урок 105. 00:09:43
    Naive Bayes Intuition (Extras)
  • Урок 106. 00:03:20
    How to get the dataset
  • Урок 107. 00:09:15
    Naive Bayes in Python
  • Урок 108. 00:14:54
    Naive Bayes in R
  • Урок 109. 00:08:09
    Decision Tree Classification Intuition
  • Урок 110. 00:03:20
    How to get the dataset
  • Урок 111. 00:12:35
    Decision Tree Classification in Python
  • Урок 112. 00:19:49
    Decision Tree Classification in R
  • Урок 113. 00:04:29
    Random Forest Classification Intuition
  • Урок 114. 00:03:20
    How to get the dataset
  • Урок 115. 00:19:55
    Random Forest Classification in Python
  • Урок 116. 00:19:57
    Random Forest Classification in R
  • Урок 117. 00:07:59
    False Positives & False Negatives
  • Урок 118. 00:04:58
    Confusion Matrix
  • Урок 119. 00:02:13
    Accuracy Paradox
  • Урок 120. 00:11:17
    CAP Curve
  • Урок 121. 00:06:20
    CAP Curve Analysis
  • Урок 122. 00:14:18
    K-Means Clustering Intuition
  • Урок 123. 00:07:49
    K-Means Random Initialization Trap
  • Урок 124. 00:11:52
    K-Means Selecting The Number Of Clusters
  • Урок 125. 00:03:20
    How to get the dataset
  • Урок 126. 00:17:56
    K-Means Clustering in Python
  • Урок 127. 00:11:48
    K-Means Clustering in R
  • Урок 128. 00:08:49
    Hierarchical Clustering Intuition
  • Урок 129. 00:08:49
    Hierarchical Clustering How Dendrograms Work
  • Урок 130. 00:11:22
    Hierarchical Clustering Using Dendrograms
  • Урок 131. 00:03:20
    How to get the dataset
  • Урок 132. 00:04:59
    HC in Python - Step 1
  • Урок 133. 00:06:34
    HC in Python - Step 2
  • Урок 134. 00:05:29
    HC in Python - Step 3
  • Урок 135. 00:04:30
    HC in Python - Step 4
  • Урок 136. 00:04:06
    HC in Python - Step 5
  • Урок 137. 00:03:46
    HC in R - Step 1
  • Урок 138. 00:05:25
    HC in R - Step 2
  • Урок 139. 00:03:20
    HC in R - Step 3
  • Урок 140. 00:02:46
    HC in R - Step 4
  • Урок 141. 00:02:34
    HC in R - Step 5
  • Урок 142. 00:18:14
    Apriori Intuition
  • Урок 143. 00:03:20
    How to get the dataset
  • Урок 144. 00:19:54
    Apriori in R - Step 1
  • Урок 145. 00:14:26
    Apriori in R - Step 2
  • Урок 146. 00:19:19
    Apriori in R - Step 3
  • Урок 147. 00:17:59
    Apriori in Python - Step 1
  • Урок 148. 00:14:39
    Apriori in Python - Step 2
  • Урок 149. 00:12:07
    Apriori in Python - Step 3
  • Урок 150. 00:06:06
    Eclat Intuition
  • Урок 151. 00:03:20
    How to get the dataset
  • Урок 152. 00:10:10
    Eclat in R
  • Урок 153. 00:15:37
    The Multi-Armed Bandit Problem
  • Урок 154. 00:14:54
    Upper Confidence Bound (UCB) Intuition
  • Урок 155. 00:03:20
    How to get the dataset
  • Урок 156. 00:14:43
    Upper Confidence Bound in Python - Step 1
  • Урок 157. 00:18:10
    Upper Confidence Bound in Python - Step 2
  • Урок 158. 00:18:48
    Upper Confidence Bound in Python - Step 3
  • Урок 159. 00:03:54
    Upper Confidence Bound in Python - Step 4
  • Урок 160. 00:13:40
    Upper Confidence Bound in R - Step 1
  • Урок 161. 00:16:00
    Upper Confidence Bound in R - Step 2
  • Урок 162. 00:17:39
    Upper Confidence Bound in R - Step 3
  • Урок 163. 00:03:19
    Upper Confidence Bound in R - Step 4
  • Урок 164. 00:19:13
    Thompson Sampling Intuition
  • Урок 165. 00:08:13
    Algorithm Comparison: UCB vs Thompson Sampling
  • Урок 166. 00:03:20
    How to get the dataset
  • Урок 167. 00:19:47
    Thompson Sampling in Python - Step 1
  • Урок 168. 00:03:44
    Thompson Sampling in Python - Step 2
  • Урок 169. 00:19:02
    Thompson Sampling in R - Step 1
  • Урок 170. 00:03:28
    Thompson Sampling in R - Step 2
  • Урок 171. 00:05:11
    Natural Language Processing Intuition
  • Урок 172. 00:03:20
    How to get the dataset
  • Урок 173. 00:12:44
    Natural Language Processing in Python - Step 1
  • Урок 174. 00:10:56
    Natural Language Processing in Python - Step 2
  • Урок 175. 00:01:42
    Natural Language Processing in Python - Step 3
  • Урок 176. 00:12:11
    Natural Language Processing in Python - Step 4
  • Урок 177. 00:07:17
    Natural Language Processing in Python - Step 5
  • Урок 178. 00:03:05
    Natural Language Processing in Python - Step 6
  • Урок 179. 00:07:24
    Natural Language Processing in Python - Step 7
  • Урок 180. 00:16:58
    Natural Language Processing in Python - Step 8
  • Урок 181. 00:06:00
    Natural Language Processing in Python - Step 9
  • Урок 182. 00:09:57
    Natural Language Processing in Python - Step 10
  • Урок 183. 00:16:36
    Natural Language Processing in R - Step 1
  • Урок 184. 00:08:40
    Natural Language Processing in R - Step 2
  • Урок 185. 00:06:29
    Natural Language Processing in R - Step 3
  • Урок 186. 00:02:59
    Natural Language Processing in R - Step 4
  • Урок 187. 00:02:06
    Natural Language Processing in R - Step 5
  • Урок 188. 00:05:50
    Natural Language Processing in R - Step 6
  • Урок 189. 00:03:28
    Natural Language Processing in R - Step 7
  • Урок 190. 00:05:21
    Natural Language Processing in R - Step 8
  • Урок 191. 00:12:51
    Natural Language Processing in R - Step 9
  • Урок 192. 00:17:32
    Natural Language Processing in R - Step 10
  • Урок 193. 00:12:35
    What is Deep Learning?
  • Урок 194. 00:02:53
    Plan of attack
  • Урок 195. 00:16:26
    The Neuron
  • Урок 196. 00:08:30
    The Activation Function
  • Урок 197. 00:12:49
    How do Neural Networks work?
  • Урок 198. 00:13:00
    How do Neural Networks learn?
  • Урок 199. 00:10:14
    Gradient Descent
  • Урок 200. 00:08:45
    Stochastic Gradient Descent
  • Урок 201. 00:05:23
    Backpropagation
  • Урок 202. 00:03:20
    How to get the dataset
  • Урок 203. 00:05:00
    Business Problem Description
  • Урок 204. 00:12:59
    ANN in Python - Step 1
  • Урок 205. 00:18:17
    ANN in Python - Step 2
  • Урок 206. 00:03:15
    ANN in Python - Step 3
  • Урок 207. 00:02:22
    ANN in Python - Step 4
  • Урок 208. 00:12:21
    ANN in Python - Step 5
  • Урок 209. 00:02:45
    ANN in Python - Step 6
  • Урок 210. 00:03:33
    ANN in Python - Step 7
  • Урок 211. 00:06:56
    ANN in Python - Step 8
  • Урок 212. 00:06:22
    ANN in Python - Step 9
  • Урок 213. 00:06:47
    ANN in Python - Step 10
  • Урок 214. 00:17:18
    ANN in R - Step 1
  • Урок 215. 00:06:31
    ANN in R - Step 2
  • Урок 216. 00:12:31
    ANN in R - Step 3
  • Урок 217. 00:14:08
    ANN in R - Step 4 (Last step)
  • Урок 218. 00:03:32
    Plan of attack
  • Урок 219. 00:15:50
    What are convolutional neural networks?
  • Урок 220. 00:16:39
    Step 1 - Convolution Operation
  • Урок 221. 00:06:42
    Step 1(b) - ReLU Layer
  • Урок 222. 00:14:14
    Step 2 - Pooling
  • Урок 223. 00:01:53
    Step 3 - Flattening
  • Урок 224. 00:19:26
    Step 4 - Full Connection
  • Урок 225. 00:04:20
    Summary
  • Урок 226. 00:18:21
    Softmax & Cross-Entropy
  • Урок 227. 00:03:20
    How to get the dataset
  • Урок 228. 00:12:46
    CNN in Python - Step 1
  • Урок 229. 00:03:01
    CNN in Python - Step 2
  • Урок 230. 00:01:06
    CNN in Python - Step 3
  • Урок 231. 00:12:52
    CNN in Python - Step 4
  • Урок 232. 00:04:59
    CNN in Python - Step 5
  • Урок 233. 00:05:00
    CNN in Python - Step 6
  • Урок 234. 00:05:58
    CNN in Python - Step 7
  • Урок 235. 00:02:50
    CNN in Python - Step 8
  • Урок 236. 00:19:45
    CNN in Python - Step 9
  • Урок 237. 00:08:29
    CNN in Python - Step 10
  • Урок 238. 00:03:50
    Principal Component Analysis (PCA) Intuition
  • Урок 239. 00:03:20
    How to get the dataset
  • Урок 240. 00:11:47
    PCA in Python - Step 1
  • Урок 241. 00:08:05
    PCA in Python - Step 2
  • Урок 242. 00:09:49
    PCA in Python - Step 3
  • Урок 243. 00:12:09
    PCA in R - Step 1
  • Урок 244. 00:11:23
    PCA in R - Step 2
  • Урок 245. 00:13:43
    PCA in R - Step 3
  • Урок 246. 00:03:51
    Linear Discriminant Analysis (LDA) Intuition
  • Урок 247. 00:03:20
    How to get the dataset
  • Урок 248. 00:18:11
    LDA in Python
  • Урок 249. 00:20:01
    LDA in R
  • Урок 250. 00:03:20
    How to get the dataset
  • Урок 251. 00:14:28
    Kernel PCA in Python
  • Урок 252. 00:20:31
    Kernel PCA in R
  • Урок 253. 00:03:20
    How to get the dataset
  • Урок 254. 00:13:46
    k-Fold Cross Validation in Python
  • Урок 255. 00:19:30
    k-Fold Cross Validation in R
  • Урок 256. 00:15:10
    Grid Search in Python - Step 1
  • Урок 257. 00:11:05
    Grid Search in Python - Step 2
  • Урок 258. 00:14:00
    Grid Search in R
  • Урок 259. 00:03:20
    How to get the dataset
  • Урок 260. 00:09:32
    XGBoost in Python - Step 1
  • Урок 261. 00:12:43
    XGBoost in Python - Step 2
  • Урок 262. 00:18:15
    XGBoost in R
  • Урок 263. 00:02:41
    THANK YOU bonus video
Этот курс находится в платной подписке. Оформи премиум подписку и смотри Machine Learning A-Z™: Hands-On Python & R In Data Science, а также все другие курсы, прямо сейчас!
Премиум FAQ