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Премиум
  • Урок 1. 00:05:26
    Introduction and Outline
  • Урок 2. 00:04:34
    Warmup (Optional)
  • Урок 3. 00:08:00
    Where to Get the Code
  • Урок 4. 00:08:57
    How to use Github & Extra Coding Tips (Optional)
  • Урок 5. 00:04:19
    Time Series Basics Section Introduction
  • Урок 6. 00:04:53
    What is a Time Series?
  • Урок 7. 00:02:31
    Modeling vs. Predicting
  • Урок 8. 00:05:57
    Why Do We Care About Shapes?
  • Урок 9. 00:06:34
    Types of Tasks
  • Урок 10. 00:06:04
    Power, Log, and Box-Cox Transformations
  • Урок 11. 00:06:06
    Power, Log, and Box-Cox Transformations in Code
  • Урок 12. 00:11:23
    Forecasting Metrics
  • Урок 13. 00:11:03
    Financial Time Series Primer
  • Урок 14. 00:03:07
    Price Simulations in Code
  • Урок 15. 00:14:36
    Random Walks and the Random Walk Hypothesis
  • Урок 16. 00:06:46
    The Naive Forecast and the Importance of Baselines
  • Урок 17. 00:07:15
    Naive Forecast and Forecasting Metrics in Code
  • Урок 18. 00:03:15
    Time Series Basics Section Summary
  • Урок 19. 00:03:11
    Suggestion Box
  • Урок 20. 00:03:03
    Exponential Smoothing Section Introduction
  • Урок 21. 00:05:38
    Exponential Smoothing Intuition for Beginners
  • Урок 22. 00:03:37
    SMA Theory
  • Урок 23. 00:08:42
    SMA Code
  • Урок 24. 00:11:08
    EWMA Theory
  • Урок 25. 00:07:40
    EWMA Code
  • Урок 26. 00:10:14
    SES Theory
  • Урок 27. 00:11:56
    SES Code
  • Урок 28. 00:07:56
    Holt's Linear Trend Model (Theory)
  • Урок 29. 00:03:14
    Holt's Linear Trend Model (Code)
  • Урок 30. 00:11:21
    Holt-Winters (Theory)
  • Урок 31. 00:07:53
    Holt-Winters (Code)
  • Урок 32. 00:09:07
    Walk-Forward Validation
  • Урок 33. 00:08:30
    Walk-Forward Validation in Code
  • Урок 34. 00:05:01
    Application: Sales Data
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    Application: Stock Predictions
  • Урок 36. 00:03:07
    SMA Application: COVID-19 Counting
  • Урок 37. 00:02:09
    SMA Application: Algorithmic Trading
  • Урок 38. 00:04:00
    Exponential Smoothing Section Summary
  • Урок 39. 00:11:23
    (Optional) More About State-Space Models
  • Урок 40. 00:05:19
    ARIMA Section Introduction
  • Урок 41. 00:12:52
    Autoregressive Models - AR(p)
  • Урок 42. 00:03:32
    Moving Average Models - MA(q)
  • Урок 43. 00:10:46
    ARIMA
  • Урок 44. 00:19:16
    ARIMA in Code
  • Урок 45. 00:13:02
    Stationarity
  • Урок 46. 00:09:51
    Stationarity in Code
  • Урок 47. 00:10:11
    ACF (Autocorrelation Function)
  • Урок 48. 00:06:56
    PACF (Partial Autocorrelation Funtion)
  • Урок 49. 00:08:27
    ACF and PACF in Code (pt 1)
  • Урок 50. 00:07:04
    ACF and PACF in Code (pt 2)
  • Урок 51. 00:09:42
    Auto ARIMA and SARIMAX
  • Урок 52. 00:09:51
    Model Selection, AIC and BIC
  • Урок 53. 00:14:05
    Auto ARIMA in Code
  • Урок 54. 00:15:46
    Auto ARIMA in Code (Stocks)
  • Урок 55. 00:07:02
    ACF and PACF for Stock Returns
  • Урок 56. 00:09:46
    Auto ARIMA in Code (Sales Data)
  • Урок 57. 00:09:15
    How to Forecast with ARIMA
  • Урок 58. 00:01:27
    Forecasting Out-Of-Sample
  • Урок 59. 00:03:32
    ARIMA Section Summary
  • Урок 60. 00:02:31
    Vector Autoregression Section Introduction
  • Урок 61. 00:13:12
    VAR and VARMA Theory
  • Урок 62. 00:07:37
    VARMA Code (pt 1)
  • Урок 63. 00:06:48
    VARMA Code (pt 2)
  • Урок 64. 00:06:26
    VARMA Code (pt 3)
  • Урок 65. 00:07:52
    VARMA Econometrics Code (pt 1)
  • Урок 66. 00:09:18
    VARMA Econometrics Code (pt 2)
  • Урок 67. 00:04:29
    Granger Causality
  • Урок 68. 00:03:20
    Granger Causality Code
  • Урок 69. 00:11:46
    Converting Between Models (Optional)
  • Урок 70. 00:03:40
    Vector Autoregression Section Summary
  • Урок 71. 00:03:53
    Machine Learning Section Introduction
  • Урок 72. 00:14:27
    Supervised Machine Learning: Classification and Regression
  • Урок 73. 00:07:35
    Autoregressive Machine Learning Models
  • Урок 74. 00:05:06
    Machine Learning Algorithms: Linear Regression
  • Урок 75. 00:06:55
    Machine Learning Algorithms: Logistic Regression
  • Урок 76. 00:10:03
    Machine Learning Algorithms: Support Vector Machines
  • Урок 77. 00:06:53
    Machine Learning Algorithms: Random Forest
  • Урок 78. 00:08:48
    Extrapolation and Stock Prices
  • Урок 79. 00:13:01
    Machine Learning for Time Series Forecasting in Code (pt 1)
  • Урок 80. 00:04:22
    Forecasting with Differencing
  • Урок 81. 00:06:48
    Machine Learning for Time Series Forecasting in Code (pt 2)
  • Урок 82. 00:05:25
    Application: Sales Data
  • Урок 83. 00:04:53
    Application: Predicting Stock Prices and Returns
  • Урок 84. 00:04:07
    Application: Predicting Stock Movements
  • Урок 85. 00:02:24
    Machine Learning Section Summary
  • Урок 86. 00:03:25
    Artificial Neural Networks: Section Introduction
  • Урок 87. 00:09:59
    The Neuron
  • Урок 88. 00:09:41
    Forward Propagation
  • Урок 89. 00:09:44
    The Geometrical Picture
  • Урок 90. 00:17:19
    Activation Functions
  • Урок 91. 00:08:42
    Multiclass Classification
  • Урок 92. 00:11:57
    ANN Code Preparation
  • Урок 93. 00:10:16
    Feedforward ANN for Time Series Forecasting Code
  • Урок 94. 00:08:51
    Feedforward ANN for Stock Return and Price Predictions Code
  • Урок 95. 00:05:54
    Human Activity Recognition Dataset
  • Урок 96. 00:06:24
    Human Activity Recognition: Code Preparation
  • Урок 97. 00:07:36
    Human Activity Recognition: Data Exploration
  • Урок 98. 00:11:00
    Human Activity Recognition: Multi-Input ANN
  • Урок 99. 00:05:57
    Human Activity Recognition: Feature-Based Model
  • Урок 100. 00:03:07
    Human Activity Recognition: Combined Model
  • Урок 101. 00:10:50
    How Does a Neural Network "Learn"?
  • Урок 102. 00:02:19
    Artificial Neural Networks: Section Summary
  • Урок 103. 00:03:08
    CNN Section Introduction
  • Урок 104. 00:16:39
    What is Convolution?
  • Урок 105. 00:05:57
    What is Convolution? (Pattern-Matching)
  • Урок 106. 00:06:56
    What is Convolution? (Weight Sharing)
  • Урок 107. 00:15:59
    Convolution on Color Images
  • Урок 108. 00:05:00
    Convolution for Time Series and ARIMA
  • Урок 109. 00:23:22
    CNN Architecture
  • Урок 110. 00:06:17
    CNN Code Preparation
  • Урок 111. 00:06:46
    CNN for Time Series Forecasting in Code
  • Урок 112. 00:06:23
    CNN for Human Activity Recognition
  • Урок 113. 00:03:15
    CNN Section Summary
  • Урок 114. 00:04:47
    RNN Section Introduction
  • Урок 115. 00:09:21
    Simple RNN / Elman Unit (pt 1)
  • Урок 116. 00:09:43
    Simple RNN / Elman Unit (pt 2)
  • Урок 117. 00:03:31
    Aside: State Space Models vs. RNNs
  • Урок 118. 00:08:39
    RNN Code Preparation
  • Урок 119. 00:08:27
    RNNs: Understanding by Implementing (Paying Attention to Shapes)
  • Урок 120. 00:17:36
    GRU and LSTM (pt 1)
  • Урок 121. 00:11:37
    GRU and LSTM (pt 2)
  • Урок 122. 00:09:29
    LSTMs for Time Series Forecasting in Code
  • Урок 123. 00:06:11
    LSTMs for Time Series Classification in Code
  • Урок 124. 00:03:19
    The Unreasonable Ineffectiveness of Recurrent Neural Networks
  • Урок 125. 00:02:58
    RNN Section Summary
  • Урок 126. 00:03:57
    GARCH Section Introduction
  • Урок 127. 00:04:58
    ARCH Theory (pt 1)
  • Урок 128. 00:07:37
    ARCH Theory (pt 2)
  • Урок 129. 00:05:16
    ARCH Theory (pt 3)
  • Урок 130. 00:07:41
    GARCH Theory
  • Урок 131. 00:07:55
    GARCH Code Preparation (pt 1)
  • Урок 132. 00:07:56
    GARCH Code Preparation (pt 2)
  • Урок 133. 00:06:08
    GARCH Code (pt 1)
  • Урок 134. 00:08:31
    GARCH Code (pt 2)
  • Урок 135. 00:07:12
    GARCH Code (pt 3)
  • Урок 136. 00:05:53
    GARCH Code (pt 4)
  • Урок 137. 00:04:21
    GARCH Code (pt 5)
  • Урок 138. 00:11:28
    A Deep Learning Approach to GARCH
  • Урок 139. 00:06:37
    GARCH Section Summary
  • Урок 140. 00:08:03
    AWS Forecast Section Introduction
  • Урок 141. 00:09:17
    Data Model
  • Урок 142. 00:04:10
    Creating an IAM Role
  • Урок 143. 00:10:00
    Code pt 1 (Getting and Transforming the Data)
  • Урок 144. 00:12:53
    Code pt 2 (Uploading the data to S3)
  • Урок 145. 00:06:53
    Code pt 3 (Building your Model)
  • Урок 146. 00:06:50
    Code pt 4 (Generating and Evaluating the Forecast)
  • Урок 147. 00:02:55
    AWS Forecast Exercise
  • Урок 148. 00:04:56
    AWS Forecast Section Summary
  • Урок 149. 00:03:12
    Prophet Section Introduction
  • Урок 150. 00:08:25
    How does Prophet work?
  • Урок 151. 00:12:42
    Prophet: Code Preparation
  • Урок 152. 00:09:00
    Prophet in Code: Data Preparation
  • Урок 153. 00:08:31
    Prophet in Code: Fit, Forecast, Plot
  • Урок 154. 00:10:20
    Prophet in Code: Holidays and Exogenous Regressors
  • Урок 155. 00:06:08
    Prophet in Code: Cross-Validation
  • Урок 156. 00:04:15
    Prophet in Code: Changepoint Detection
  • Урок 157. 00:10:17
    Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data
  • Урок 158. 00:13:11
    (The Dangers of) Prophet for Stock Price Prediction
  • Урок 159. 00:03:28
    Prophet Section Summary
  • Урок 160. 00:20:21
    Anaconda Environment Setup
  • Урок 161. 00:17:23
    How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
  • Урок 162. 00:15:57
    How to Code by Yourself (part 1)
  • Урок 163. 00:09:24
    How to Code by Yourself (part 2)
  • Урок 164. 00:12:30
    Proof that using Jupyter Notebook is the same as not using it
  • Урок 165. 00:10:25
    How to Succeed in this Course (Long Version)
  • Урок 166. 00:22:05
    Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Урок 167. 00:11:19
    Machine Learning and AI Prerequisite Roadmap (pt 1)
  • Урок 168. 00:16:08
    Machine Learning and AI Prerequisite Roadmap (pt 2)
  • Урок 169. 00:02:49
    What is the Appendix?
  • Урок 170. 00:05:32
    BONUS Lecture