1. Урок 1. 00:13:59
    Introduction to the course
  2. Урок 2. 00:09:02
    Introduction to Kaggle
  3. Урок 3. 00:09:02
    Installation of Python and Anaconda
  4. Урок 4. 00:03:34
    Python Introduction
  5. Урок 5. 00:15:05
    Variables in Python
  6. Урок 6. 00:05:28
    Numeric Operations in Python
  7. Урок 7. 00:02:25
    Logical Operations
  8. Урок 8. 00:08:16
    If else Loop
  9. Урок 9. 00:10:18
    for while Loop
  10. Урок 10. 00:11:19
    Functions
  11. Урок 11. 00:12:43
    String Part1
  12. Урок 12. 00:03:02
    String Part2
  13. Урок 13. 00:03:06
    List Part1
  14. Урок 14. 00:10:49
    List Part2
  15. Урок 15. 00:08:53
    List Part3
  16. Урок 16. 00:08:11
    List Part4
  17. Урок 17. 00:08:42
    Tuples
  18. Урок 18. 00:07:28
    Sets
  19. Урок 19. 00:07:36
    Dictionaries
  20. Урок 20. 00:07:09
    Comprehentions
  21. Урок 21. 00:06:20
    Introduction
  22. Урок 22. 00:19:21
    Numpy Operations Part1
  23. Урок 23. 00:24:27
    Numpy Operations Part2
  24. Урок 24. 00:06:30
    Introduction
  25. Урок 25. 00:07:59
    Series
  26. Урок 26. 00:07:54
    DataFrame
  27. Урок 27. 00:01:24
    Operations Part1
  28. Урок 28. 00:05:11
    Operations Part2
  29. Урок 29. 00:06:07
    Indexes
  30. Урок 30. 00:07:28
    loc and iloc
  31. Урок 31. 00:05:29
    Reading CSV
  32. Урок 32. 00:03:44
    Merging Part1
  33. Урок 33. 00:05:26
    groupby
  34. Урок 34. 00:04:26
    Merging Part2
  35. Урок 35. 00:03:25
    Pivot Table
  36. Урок 36. 00:43:18
    Linear Algebra : Vectors
  37. Урок 37. 00:15:44
    Linear Algebra : Matrix Part1
  38. Урок 38. 00:16:22
    Linear Algebra : Matrix Part2
  39. Урок 39. 00:08:45
    Linear Algebra : Going From 2D to nD Part1
  40. Урок 40. 00:06:54
    Linear Algebra : 2D to nD Part2
  41. Урок 41. 00:03:02
    Inferential Statistics
  42. Урок 42. 00:13:16
    Probability Theory
  43. Урок 43. 00:05:00
    Probability Distribution
  44. Урок 44. 00:04:53
    Expected Values Part1
  45. Урок 45. 00:03:15
    Expected Values Part2
  46. Урок 46. 00:06:02
    Without Experiment
  47. Урок 47. 00:04:12
    Binomial Distribution
  48. Урок 48. 00:02:25
    Commulative Distribution
  49. Урок 49. 00:04:44
    PDF
  50. Урок 50. 00:05:01
    Normal Distribution
  51. Урок 51. 00:04:45
    z Score
  52. Урок 52. 00:09:42
    Sampling
  53. Урок 53. 00:06:17
    Sampling Distribution
  54. Урок 54. 00:03:08
    Central Limit Theorem
  55. Урок 55. 00:07:15
    Confidence Interval Part1
  56. Урок 56. 00:03:19
    Confidence Interval Part2
  57. Урок 57. 00:08:30
    Introduction
  58. Урок 58. 00:06:29
    NULL And Alternate Hypothesis
  59. Урок 59. 00:05:47
    Examples
  60. Урок 60. 00:08:02
    One/Two Tailed Tests
  61. Урок 61. 00:04:19
    Critical Value Method
  62. Урок 62. 00:07:37
    z Table
  63. Урок 63. 00:03:18
    Examples
  64. Урок 64. 00:03:03
    More Examples
  65. Урок 65. 00:05:16
    p Value
  66. Урок 66. 00:02:54
    Types of Error
  67. Урок 67. 00:03:28
    t- distribution Part1
  68. Урок 68. 00:02:43
    t- distribution Part2
  69. Урок 69. 00:19:55
    Matplotlib
  70. Урок 70. 00:20:26
    Seaborn
  71. Урок 71. 00:10:24
    Case Study
  72. Урок 72. 00:04:27
    Seaborn On Time Series Data
  73. Урок 73. 00:01:07
    Introduction
  74. Урок 74. 00:05:07
    Data Sourcing and Cleaning part1
  75. Урок 75. 00:03:15
    Data Sourcing and Cleaning part2
  76. Урок 76. 00:04:00
    Data Sourcing and Cleaning part3
  77. Урок 77. 00:03:57
    Data Sourcing and Cleaning part4
  78. Урок 78. 00:03:31
    Data Sourcing and Cleaning part5
  79. Урок 79. 00:04:15
    Data Sourcing and Cleaning part6
  80. Урок 80. 00:14:42
    Data Cleaning part1
  81. Урок 81. 00:09:27
    Data Cleaning part2
  82. Урок 82. 00:22:23
    Univariate Analysis Part1
  83. Урок 83. 00:17:33
    Univariate Analysis Part2
  84. Урок 84. 00:06:47
    Segmented Analysis
  85. Урок 85. 00:13:00
    Bivariate Analysis
  86. Урок 86. 00:12:15
    Derived Columns
  87. Урок 87. 00:02:14
    Introduction to Machine Learning
  88. Урок 88. 00:08:57
    Types of Machine Learning
  89. Урок 89. 00:03:06
    Introduction to Linear Regression (LR)
  90. Урок 90. 00:09:18
    How LR Works?
  91. Урок 91. 00:09:30
    Some Fun With Maths Behind LR
  92. Урок 92. 00:10:54
    R Square
  93. Урок 93. 00:14:49
    LR Case Study Part1
  94. Урок 94. 00:04:54
    LR Case Study Part2
  95. Урок 95. 00:04:26
    LR Case Study Part3
  96. Урок 96. 00:01:04
    Residual Square Error (RSE)
  97. Урок 97. 00:03:16
    Introduction
  98. Урок 98. 00:07:38
    Case Study part1
  99. Урок 99. 00:10:38
    Case Study part2
  100. Урок 100. 00:06:05
    Case Study part3
  101. Урок 101. 00:00:46
    Adjusted R Square
  102. Урок 102. 00:07:09
    Case Study Part1
  103. Урок 103. 00:09:18
    Case Study Part2
  104. Урок 104. 00:06:37
    Case Study Part3
  105. Урок 105. 00:14:39
    Case Study Part4
  106. Урок 106. 00:04:52
    Case Study Part5
  107. Урок 107. 00:06:22
    Case Study Part6 (RFE)
  108. Урок 108. 00:05:18
    Introduction to the Problem Statement
  109. Урок 109. 00:09:30
    Playing With Data
  110. Урок 110. 00:04:43
    Building Model Part1
  111. Урок 111. 00:07:41
    Building Model Part2
  112. Урок 112. 00:03:52
    Building Model Part3
  113. Урок 113. 00:03:36
    Verification of Model
  114. Урок 114. 00:15:58
    Pre-Req For Gradient Descent Part1
  115. Урок 115. 00:09:00
    Pre-Req For Gradient Descent Part2
  116. Урок 116. 00:02:22
    Cost Functions
  117. Урок 117. 00:07:26
    Defining Cost Functions More Formally
  118. Урок 118. 00:10:51
    Gradient Descent
  119. Урок 119. 00:04:14
    Optimisation
  120. Урок 120. 00:04:53
    Closed Form Vs Gradient Descent
  121. Урок 121. 00:05:40
    Gradient Descent case study
  122. Урок 122. 00:12:55
    Introduction to Classification
  123. Урок 123. 00:07:31
    Defining Classification Mathematically
  124. Урок 124. 00:11:34
    Introduction to KNN
  125. Урок 125. 00:12:45
    Accuracy of KNN
  126. Урок 126. 00:12:54
    Effectiveness of KNN
  127. Урок 127. 00:12:21
    Distance Metrics
  128. Урок 128. 00:08:31
    Distance Metrics Part2
  129. Урок 129. 00:09:36
    Finding k
  130. Урок 130. 00:02:53
    KNN on Regression
  131. Урок 131. 00:07:56
    Case Study
  132. Урок 132. 00:22:16
    Classification Case1
  133. Урок 133. 00:15:03
    Classification Case2
  134. Урок 134. 00:13:35
    Classification Case3
  135. Урок 135. 00:12:38
    Classification Case4
  136. Урок 136. 00:21:16
    Performance Metrics Part1
  137. Урок 137. 00:15:17
    Performance Metrics Part2
  138. Урок 138. 00:05:09
    Performance Metrics Part3
  139. Урок 139. 00:11:37
    Model Creation Case1
  140. Урок 140. 00:07:39
    Model Creation Case2
  141. Урок 141. 00:11:36
    Gridsearch Case study Part1
  142. Урок 142. 00:15:03
    Gridsearch Case study Part2
  143. Урок 143. 00:14:58
    Introduction to Naive Bayes
  144. Урок 144. 00:10:55
    Bayes Theorem
  145. Урок 145. 00:08:45
    Practical Example from NB with One Column
  146. Урок 146. 00:11:31
    Practical Example from NB with Multiple Columns
  147. Урок 147. 00:08:43
    Naive Bayes On Text Data Part1
  148. Урок 148. 00:05:11
    Naive Bayes On Text Data Part2
  149. Урок 149. 00:04:11
    Laplace Smoothing
  150. Урок 150. 00:01:38
    Bernoulli Naive Bayes
  151. Урок 151. 00:08:41
    Case Study 1
  152. Урок 152. 00:06:52
    Case Study 2 Part1
  153. Урок 153. 00:02:10
    Case Study 2 Part2
  154. Урок 154. 00:07:31
    Introduction
  155. Урок 155. 00:10:19
    Sigmoid Function
  156. Урок 156. 00:10:01
    Log Odds
  157. Урок 157. 00:16:29
    Case Study
  158. Урок 158. 00:15:06
    Introduction
  159. Урок 159. 00:06:28
    Hyperplane Part1
  160. Урок 160. 00:14:06
    Hyperplane Part2
  161. Урок 161. 00:07:38
    Maths Behind SVM
  162. Урок 162. 00:04:04
    Support Vectors
  163. Урок 163. 00:09:59
    Slack Variable
  164. Урок 164. 00:06:25
    SVM Case Study Part1
  165. Урок 165. 00:06:49
    SVM Case Study Part2
  166. Урок 166. 00:08:55
    Kernel Part1
  167. Урок 167. 00:12:34
    Kernel Part2
  168. Урок 168. 00:07:28
    Case Study : 2
  169. Урок 169. 00:08:46
    Case Study : 3 Part1
  170. Урок 170. 00:05:24
    Case Study : 3 Part2
  171. Урок 171. 00:16:33
    Case Study 4
  172. Урок 172. 00:07:21
    Introduction
  173. Урок 173. 00:07:51
    Example of DT
  174. Урок 174. 00:05:02
    Homogenity
  175. Урок 175. 00:07:05
    Gini Index
  176. Урок 176. 00:05:24
    Information Gain Part1
  177. Урок 177. 00:05:14
    Information Gain Part2
  178. Урок 178. 00:04:11
    Advantages and Disadvantages of DT
  179. Урок 179. 00:09:59
    Preventing Overfitting Issues in DT
  180. Урок 180. 00:10:36
    DT Case Study Part1
  181. Урок 181. 00:09:06
    DT Case Study Part2
  182. Урок 182. 00:10:15
    Introduction to Ensembles
  183. Урок 183. 00:13:10
    Bagging
  184. Урок 184. 00:04:39
    Advantages
  185. Урок 185. 00:03:53
    Runtime
  186. Урок 186. 00:05:41
    Case study
  187. Урок 187. 00:06:06
    Introduction to Boosting
  188. Урок 188. 00:02:54
    Weak Learners
  189. Урок 189. 00:02:31
    Shallow Decision Tree
  190. Урок 190. 00:07:49
    Adaboost Part1
  191. Урок 191. 00:06:45
    Adaboost Part2
  192. Урок 192. 00:04:47
    Adaboost Case Study
  193. Урок 193. 00:04:28
    XGBoost
  194. Урок 194. 00:03:10
    Boosting Part1
  195. Урок 195. 00:06:49
    Boosting Part2
  196. Урок 196. 00:08:36
    XGboost Algorithm
  197. Урок 197. 00:09:40
    Case Study Part1
  198. Урок 198. 00:10:45
    Case Study Part2
  199. Урок 199. 00:05:34
    Case Study Part3
  200. Урок 200. 00:21:29
    Model Selection Part1
  201. Урок 201. 00:12:32
    Model Selection Part2
  202. Урок 202. 00:09:42
    Model Selection Part3
  203. Урок 203. 00:10:38
    Introduction to Clustering
  204. Урок 204. 00:07:22
    Segmentation
  205. Урок 205. 00:08:08
    Kmeans
  206. Урок 206. 00:10:23
    Maths Behind Kmeans
  207. Урок 207. 00:02:22
    More Maths
  208. Урок 208. 00:10:11
    Kmeans plus
  209. Урок 209. 00:06:44
    Value of K
  210. Урок 210. 00:02:32
    Hopkins test
  211. Урок 211. 00:10:56
    Case Study Part1
  212. Урок 212. 00:06:48
    Case Study Part2
  213. Урок 213. 00:04:13
    More on Segmentation
  214. Урок 214. 00:07:34
    Hierarchial Clustering
  215. Урок 215. 00:05:35
    Case Study
  216. Урок 216. 00:30:26
    Introduction
  217. Урок 217. 00:25:59
    PCA
  218. Урок 218. 00:24:25
    Maths Behind PCA
  219. Урок 219. 00:05:16
    Case Study Part1
  220. Урок 220. 00:15:27
    Case Study Part2
  221. Урок 221. 00:07:20
    Introduction
  222. Урок 222. 00:05:24
    Example Part1
  223. Урок 223. 00:09:07
    Example Part2
  224. Урок 224. 00:15:23
    Optimal Solution
  225. Урок 225. 00:03:25
    Case study
  226. Урок 226. 00:09:01
    Regularization
  227. Урок 227. 00:07:03
    Ridge and Lasso
  228. Урок 228. 00:08:51
    Case Study
  229. Урок 229. 00:05:32
    Model Selection
  230. Урок 230. 00:03:20
    Adjusted R Square
  231. Урок 231. 00:02:42
    Expectations
  232. Урок 232. 00:09:13
    Introduction
  233. Урок 233. 00:15:39
    History
  234. Урок 234. 00:07:18
    Perceptron
  235. Урок 235. 00:13:07
    Multi Layered Perceptron
  236. Урок 236. 00:10:27
    Neural Network Playground
  237. Урок 237. 00:08:41
    Introduction to the Problem Statement
  238. Урок 238. 00:14:34
    Playing With The Data
  239. Урок 239. 00:09:54
    Translating the Problem In Machine Learning World
  240. Урок 240. 00:08:02
    Dealing with Text Data
  241. Урок 241. 00:10:24
    Train, Test And Cross Validation Split
  242. Урок 242. 00:16:56
    Understanding Evaluation Matrix: Log Loss
  243. Урок 243. 00:08:43
    Building A Worst Model
  244. Урок 244. 00:05:49
    Evaluating Worst ML Model
  245. Урок 245. 00:12:14
    First Categorical column analysis
  246. Урок 246. 00:05:07
    Response encoding and one hot encoder
  247. Урок 247. 00:12:06
    Laplace Smoothing and Calibrated classifier
  248. Урок 248. 00:06:54
    Significance of first categorical column
  249. Урок 249. 00:04:08
    Second Categorical column
  250. Урок 250. 00:06:53
    Third Categorical column
  251. Урок 251. 00:04:24
    Data pre-processing before building machine learning model
  252. Урок 252. 00:13:12
    Building Machine Learning model :part1
  253. Урок 253. 00:11:39
    Building Machine Learning model :part2
  254. Урок 254. 00:03:18
    Building Machine Learning model :part3
  255. Урок 255. 00:03:14
    Building Machine Learning model :part4
  256. Урок 256. 00:03:49
    Building Machine Learning model :part5
  257. Урок 257. 00:06:33
    Building Machine Learning model :part6