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