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