Section 1 : Welcome

Lecture 1 Introduction and Outline 5:12
Lecture 2 Warmup (Optional) 4:23

Section 2 : Getting Set Up

Lecture 3 Where to Get the Code 7:48
Lecture 4 How to use Github & Extra Coding Tips (Optional) 8:45

Section 3 : Time Series Basics

Lecture 5 Time Series Basics Section Introduction 4:8
Lecture 6 What is a Time Series 4:42
Lecture 7 Modeling vs 2:20
Lecture 8 Why Do We Care About Shapes
Lecture 9 Types of Tasks 6:23
Lecture 10 Power, Log, and Box-Cox Transformations 5:54
Lecture 11 Power, Log, and Box-Cox Transformations in Code 5:54
Lecture 12 Forecasting Metrics 11:12
Lecture 13 Financial Time Series Primer 10:51
Lecture 14 Price Simulations in Code 2:55
Lecture 15 Random Walks and the Random Walk Hypothesis 14:25
Lecture 16 The Naive Forecast and the Importance of Baselines 6:35
Lecture 17 Naive Forecast and Forecasting Metrics in Code 7:4
Lecture 18 Time Series Basics Section Summary 3:4
Lecture 19 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 4 : Exponential Smoothing and ETS Methods

Lecture 20 Exponential Smoothing Section Introduction 2:51
Lecture 21 Exponential Smoothing Intuition for Beginners 5:27
Lecture 22 SMA Theory 3:26
Lecture 23 SMA Code 8:30
Lecture 24 EWMA Theory 10:57
Lecture 25 EWMA Code 7:28
Lecture 26 SES Theory 10:3
Lecture 27 SES Code 11:44
Lecture 28 Holt's Linear Trend Model (Theory) 7:44
Lecture 29 Holt's Linear Trend Model (Code) 3:2
Lecture 30 Holt-Winters (Theory) 11:9
Lecture 31 Holt-Winters (Code) 7:42
Lecture 32 Walk-Forward Validation 8:56
Lecture 33 Walk-Forward Validation in Code 8:19
Lecture 34 Application Sales Data 4:50
Lecture 35 Application Stock Predictions 5:27
Lecture 36 SMA Application COVID-19 Counting 2:55
Lecture 37 SMA Application Algorithmic Trading 1:58
Lecture 38 Exponential Smoothing Section Summary 3:49
Lecture 39 (Optional) More About State-Space Models 11:11

Section 5 : ARIMA

Lecture 40 ARIMA Section Introduction 5:8
Lecture 41 Autoregressive Models - AR(p) 12:40
Lecture 42 Moving Average Models - MA(q) 3:20
Lecture 43 ARIMA 10:34
Lecture 44 ARIMA in Code 19:4
Lecture 45 Stationarity 12:51
Lecture 46 Stationarity in Code 9:39
Lecture 47 ACF (Autocorrelation Function) 10:0
Lecture 48 PACF (Partial Autocorrelation Funtion) 6:44
Lecture 49 ACF and PACF in Code (pt 1) 8:16
Lecture 50 ACF and PACF in Code (pt 2)
Lecture 51 Auto ARIMA and SARIMAX 9:31
Lecture 52 Model Selection, AIC and BIC
Lecture 53 Auto ARIMA in Code
Lecture 54 Auto ARIMA in Code (Stocks) 15:34
Lecture 55 ACF and PACF for Stock Returns 6:50
Lecture 56 Auto ARIMA in Code (Sales Data) 9:34
Lecture 57 How to Forecast with ARIMA 9:3
Lecture 58 Forecasting Out-Of-Sample 1:15
Lecture 59 ARIMA Section Summary 3:21

Section 6 : Vector Autoregression (VAR, VMA, VARMA)

Lecture 60 Vector Autoregression Section Introduction 2:20
Lecture 61 VAR and VARMA Theory 13:0
Lecture 62 VARMA Code (pt 1) 7:25
Lecture 63 VARMA Code (pt 2) 6:36
Lecture 64 VARMA Code (pt 3) 6:14
Lecture 65 VARMA Econometrics Code (pt 1) 7:40
Lecture 66 VARMA Econometrics Code (pt 2) 9:7
Lecture 67 Granger Causality 4:17
Lecture 68 Granger Causality Code 3:9
Lecture 69 Converting Between Models (Optional) 11:34
Lecture 70 Vector Autoregression Section Summary 3:28

Section 7 : Machine Learning Methods

Lecture 71 Machine Learning Section Introduction 3:41
Lecture 72 Supervised Machine Learning Classification and Regression 14:15
Lecture 73 Autoregressive Machine Learning Models 7:23
Lecture 74 Machine Learning Algorithms Linear Regression 4:54
Lecture 75 Machine Learning Algorithms Logistic Regression 6:44
Lecture 76 Machine Learning Algorithms Support Vector Machines 9:51
Lecture 77 Machine Learning Algorithms Random Forest 6:40
Lecture 78 Extrapolation and Stock Prices 8:36
Lecture 79 Machine Learning for Time Series Forecasting in Code (pt 1) 12:49
Lecture 80 Forecasting with Differencing 4:11
Lecture 81 Machine Learning for Time Series Forecasting in Code (pt 2) 6:37
Lecture 82 Application Sales Data 5:13
Lecture 83 Application Predicting Stock Prices and Returns 4:41
Lecture 84 Application Predicting Stock Movements
Lecture 85 Machine Learning Section Summary 2:12

Section 8 : Deep Learning Artificial Neural Networks (ANN)

Lecture 86 Artificial Neural Networks Section Introduction 3:13
Lecture 87 The Neuron 9:47
Lecture 88 Forward Propagation 9:29
Lecture 89 The Geometrical Picture 9:33
Lecture 90 Activation Functions 17:7
Lecture 91 Multiclass Classification 8:30
Lecture 92 ANN Code Preparation 11:45
Lecture 93 Feedforward ANN for Time Series Forecasting Code 10:5
Lecture 94 Feedforward ANN for Stock Return and Price Predictions Code 8:39
Lecture 95 Human Activity Recognition Dataset 5:43
Lecture 96 Human Activity Recognition Code Preparation 6:12
Lecture 97 Human Activity Recognition Data Exploration 7:24
Lecture 98 Human Activity Recognition Multi-Input ANN 10:48
Lecture 99 Human Activity Recognition Feature-Based Model 5:45
Lecture 100 Human Activity Recognition Combined Model 2:55
Lecture 101 How Does a Neural Network Learn 10:38
Lecture 102 Artificial Neural Networks Section Summary 2:7

Section 9 : Deep Learning Convolutional Neural Networks (CNN)

Lecture 103 CNN Section Introduction 2:57
Lecture 104 What is Convolution 16:27
Lecture 105 What is Convolution (Pattern-Matching) 5:45
Lecture 106 What is Convolution (Weight Sharing) 6:44
Lecture 107 Convolution on Color Images 15:47
Lecture 108 Convolution for Time Series and ARIMA 4:49
Lecture 109 CNN Architecture 23:11
Lecture 110 CNN Code Preparation 6:5
Lecture 111 CNN for Time Series Forecasting in Code 6:34
Lecture 112 CNN for Human Activity Recognition 6:11
Lecture 113 CNN Section Summary 3:4

Section 10 : Deep Learning Recurrent Neural Networks (RNN)

Lecture 114 RNN Section Introduction 4:35
Lecture 115 Simple RNN Elman Unit (pt 1) 9:9
Lecture 116 Simple RNN Elman Unit (pt 2) 9:31
Lecture 117 Aside State Space Models vs 3:20
Lecture 118 RNN Code Preparation 8:27
Lecture 119 RNNs Understanding by Implementing (Paying Attention to Shapes) 8:15
Lecture 120 GRU and LSTM (pt 1) 17:22
Lecture 121 GRU and LSTM (pt 2) 11:25
Lecture 122 LSTMs for Time Series Forecasting in Code 9:11
Lecture 123 LSTMs for Time Series Classification in Code 5:53
Lecture 124 The Unreasonable Ineffectiveness of Recurrent Neural Networks 3:7
Lecture 125 RNN Section Summary 0:0

Section 11 : VIP GARCH

Lecture 126 GARCH Section Introduction 3:45
Lecture 127 ARCH Theory (pt 1) 4:46
Lecture 128 ARCH Theory (pt 2) 7:26
Lecture 129 ARCH Theory (pt 3) 5:4
Lecture 130 GARCH Theory 7:30
Lecture 131 GARCH Code Preparation (pt 1) 7:43
Lecture 132 GARCH Code Preparation (pt 2) 7:46
Lecture 133 GARCH Code (pt 1) 5:56
Lecture 134 GARCH Code (pt 2) 8:19
Lecture 135 GARCH Code (pt 3) 7:0
Lecture 136 GARCH Code (pt 4) 5:41
Lecture 137 GARCH Code (pt 5) 4:9
Lecture 138 A Deep Learning Approach to GARCH 11:16
Lecture 139 GARCH Section Summary 6:25

Section 12 : VIP AWS Forecast

Lecture 140 AWS Forecast Section Introduction 7:51
Lecture 141 Data Model 9:6
Lecture 142 Creating an IAM Role 3:57
Lecture 143 Code pt 1 (Getting and Transforming the Data) 9:47
Lecture 144 Code pt 2 (Uploading the data to S3) 12:40
Lecture 145 Code pt 3 (Building your Model) 6:41
Lecture 146 Code pt 4 (Generating and Evaluating the Forecast) 6:39
Lecture 147 AWS Forecast Exercise 2:44
Lecture 148 AWS Forecast Section Summary 4:45

Section 13 : VIP Facebook Prophet

Lecture 149 Prophet Section Introduction 3:1
Lecture 150 How does Prophet work 8:15
Lecture 151 Prophet Code Preparation 12:31
Lecture 152 Prophet in Code Data Preparation 8:50
Lecture 153 Prophet in Code Fit, Forecast, Plot 8:19
Lecture 154 Prophet in Code Holidays and Exogenous Regressors 10:9
Lecture 155 Prophet in Code Cross-Validation 5:56
Lecture 156 Prophet in Code Changepoint Detection 4:4
Lecture 157 Prophet Multiplicative Seasonality, Outliers, Non-Daily Data 10:5
Lecture 158 (The Dangers of) Prophet for Stock Price Prediction 13:0
Lecture 159 Prophet Section Summary 3:17

Section 14 : Setting Up Your Environment FAQ

Lecture 160 Anaconda Environment Setup 20:13
Lecture 161 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:11

Section 15 : Extra Help With Python Coding for Beginners FAQ

Lecture 162 How to Code by Yourself (part 1) 15:50
Lecture 163 How to Code by Yourself (part 2) 9:22
Lecture 164 Proof that using Jupyter Notebook is the same as not using it 12:24

Section 16 : Effective Learning Strategies for Machine Learning FAQ

Lecture 165 How to Succeed in this Course (Long Version) 10:17
Lecture 166 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:57
Lecture 167 About Certification Pdf
Lecture 168 About Proctor Testing Pdf

Section 17 : Appendix FAQ Finale

Lecture 169 What is the Appendix 2:41