Section 1 : Welcome
Lecture 1 Introduction and Outline 00:05:12 Duration
Lecture 2 Warmup (Optional) 00:04:23 Duration

Section 2 : Getting Set Up
Lecture 1 Where to Get the Code 00:07:48 Duration
Lecture 2 How to use Github & Extra Coding Tips (Optional) 00:08:45 Duration

Section 3 : Time Series Basics
Lecture 1 Time Series Basics Section Introduction 00:04:08 Duration
Lecture 2 What is a Time Series 00:04:42 Duration
Lecture 3 Modeling vs 00:02:20 Duration
Lecture 4 Why Do We Care About Shapes
Lecture 5 Types of Tasks 00:06:23 Duration
Lecture 6 Power, Log, and Box-Cox Transformations 00:05:54 Duration
Lecture 7 Power, Log, and Box-Cox Transformations in Code 00:05:54 Duration
Lecture 8 Forecasting Metrics 00:11:12 Duration
Lecture 9 Financial Time Series Primer 00:10:51 Duration
Lecture 10 Price Simulations in Code 00:02:55 Duration
Lecture 11 Random Walks and the Random Walk Hypothesis 00:14:25 Duration
Lecture 12 The Naive Forecast and the Importance of Baselines 00:06:35 Duration
Lecture 13 Naive Forecast and Forecasting Metrics in Code 00:07:04 Duration
Lecture 14 Time Series Basics Section Summary 00:03:04 Duration
Lecture 15 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 4 : Exponential Smoothing and ETS Methods
Lecture 1 Exponential Smoothing Section Introduction 00:02:51 Duration
Lecture 2 Exponential Smoothing Intuition for Beginners 00:05:27 Duration
Lecture 3 SMA Theory 00:03:26 Duration
Lecture 4 SMA Code 00:08:30 Duration
Lecture 5 EWMA Theory 00:10:57 Duration
Lecture 6 EWMA Code 00:07:28 Duration
Lecture 7 SES Theory 00:10:03 Duration
Lecture 8 SES Code 00:11:44 Duration
Lecture 9 Holt's Linear Trend Model (Theory) 00:07:44 Duration
Lecture 10 Holt's Linear Trend Model (Code) 00:03:02 Duration
Lecture 11 Holt-Winters (Theory) 00:11:09 Duration
Lecture 12 Holt-Winters (Code) 00:07:42 Duration
Lecture 13 Walk-Forward Validation 00:08:56 Duration
Lecture 14 Walk-Forward Validation in Code 00:08:19 Duration
Lecture 15 Application Sales Data 00:04:50 Duration
Lecture 16 Application Stock Predictions 00:05:27 Duration
Lecture 17 SMA Application COVID-19 Counting 00:02:55 Duration
Lecture 18 SMA Application Algorithmic Trading 00:01:58 Duration
Lecture 19 Exponential Smoothing Section Summary 00:03:49 Duration
Lecture 20 (Optional) More About State-Space Models 00:11:11 Duration

Section 5 : ARIMA
Lecture 1 ARIMA Section Introduction 00:05:08 Duration
Lecture 2 Autoregressive Models - AR(p) 00:12:40 Duration
Lecture 3 Moving Average Models - MA(q) 00:03:20 Duration
Lecture 4 ARIMA 00:10:34 Duration
Lecture 5 ARIMA in Code 00:19:04 Duration
Lecture 6 Stationarity 00:12:51 Duration
Lecture 7 Stationarity in Code 00:09:39 Duration
Lecture 8 ACF (Autocorrelation Function) 00:10:00 Duration
Lecture 9 PACF (Partial Autocorrelation Funtion) 00:06:44 Duration
Lecture 10 ACF and PACF in Code (pt 1) 00:08:16 Duration
Lecture 11 ACF and PACF in Code (pt 2)
Lecture 12 Auto ARIMA and SARIMAX 00:09:31 Duration
Lecture 13 Model Selection, AIC and BIC
Lecture 14 Auto ARIMA in Code
Lecture 15 Auto ARIMA in Code (Stocks) 00:15:34 Duration
Lecture 16 ACF and PACF for Stock Returns 00:06:50 Duration
Lecture 17 Auto ARIMA in Code (Sales Data) 00:09:34 Duration
Lecture 18 How to Forecast with ARIMA 00:09:03 Duration
Lecture 19 Forecasting Out-Of-Sample 00:01:15 Duration
Lecture 20 ARIMA Section Summary 00:03:21 Duration

Section 6 : Vector Autoregression (VAR, VMA, VARMA)
Lecture 1 Vector Autoregression Section Introduction 00:02:20 Duration
Lecture 2 VAR and VARMA Theory 00:13:00 Duration
Lecture 3 VARMA Code (pt 1) 00:07:25 Duration
Lecture 4 VARMA Code (pt 2) 00:06:36 Duration
Lecture 5 VARMA Code (pt 3) 00:06:14 Duration
Lecture 6 VARMA Econometrics Code (pt 1) 00:07:40 Duration
Lecture 7 VARMA Econometrics Code (pt 2) 00:09:07 Duration
Lecture 8 Granger Causality 00:04:17 Duration
Lecture 9 Granger Causality Code 00:03:09 Duration
Lecture 10 Converting Between Models (Optional) 00:11:34 Duration
Lecture 11 Vector Autoregression Section Summary 00:03:28 Duration

Section 7 : Machine Learning Methods
Lecture 1 Machine Learning Section Introduction 00:03:41 Duration
Lecture 2 Supervised Machine Learning Classification and Regression 00:14:15 Duration
Lecture 3 Autoregressive Machine Learning Models 00:07:23 Duration
Lecture 4 Machine Learning Algorithms Linear Regression 00:04:54 Duration
Lecture 5 Machine Learning Algorithms Logistic Regression 00:06:44 Duration
Lecture 6 Machine Learning Algorithms Support Vector Machines 00:09:51 Duration
Lecture 7 Machine Learning Algorithms Random Forest 00:06:40 Duration
Lecture 8 Extrapolation and Stock Prices 00:08:36 Duration
Lecture 9 Machine Learning for Time Series Forecasting in Code (pt 1) 00:12:49 Duration
Lecture 10 Forecasting with Differencing 00:04:11 Duration
Lecture 11 Machine Learning for Time Series Forecasting in Code (pt 2) 00:06:37 Duration
Lecture 12 Application Sales Data 00:05:13 Duration
Lecture 13 Application Predicting Stock Prices and Returns 00:04:41 Duration
Lecture 14 Application Predicting Stock Movements
Lecture 15 Machine Learning Section Summary 00:02:12 Duration

Section 8 : Deep Learning Artificial Neural Networks (ANN)
Lecture 1 Artificial Neural Networks Section Introduction 00:03:13 Duration
Lecture 2 The Neuron 00:09:47 Duration
Lecture 3 Forward Propagation 00:09:29 Duration
Lecture 4 The Geometrical Picture 00:09:33 Duration
Lecture 5 Activation Functions 00:17:07 Duration
Lecture 6 Multiclass Classification 00:08:30 Duration
Lecture 7 ANN Code Preparation 00:11:45 Duration
Lecture 8 Feedforward ANN for Time Series Forecasting Code 00:10:05 Duration
Lecture 9 Feedforward ANN for Stock Return and Price Predictions Code 00:08:39 Duration
Lecture 10 Human Activity Recognition Dataset 00:05:43 Duration
Lecture 11 Human Activity Recognition Code Preparation 00:06:12 Duration
Lecture 12 Human Activity Recognition Data Exploration 00:07:24 Duration
Lecture 13 Human Activity Recognition Multi-Input ANN 00:10:48 Duration
Lecture 14 Human Activity Recognition Feature-Based Model 00:05:45 Duration
Lecture 15 Human Activity Recognition Combined Model 00:02:55 Duration
Lecture 16 How Does a Neural Network Learn 00:10:38 Duration
Lecture 17 Artificial Neural Networks Section Summary 00:02:07 Duration

Section 9 : Deep Learning Convolutional Neural Networks (CNN)
Lecture 1 CNN Section Introduction 00:02:57 Duration
Lecture 2 What is Convolution 00:16:27 Duration
Lecture 3 What is Convolution (Pattern-Matching) 00:05:45 Duration
Lecture 4 What is Convolution (Weight Sharing) 00:06:44 Duration
Lecture 5 Convolution on Color Images 00:15:47 Duration
Lecture 6 Convolution for Time Series and ARIMA 00:04:49 Duration
Lecture 7 CNN Architecture 00:23:11 Duration
Lecture 8 CNN Code Preparation 00:06:05 Duration
Lecture 9 CNN for Time Series Forecasting in Code 00:06:34 Duration
Lecture 10 CNN for Human Activity Recognition 00:06:11 Duration
Lecture 11 CNN Section Summary 00:03:04 Duration

Section 10 : Deep Learning Recurrent Neural Networks (RNN)
Lecture 1 RNN Section Introduction 00:04:35 Duration
Lecture 2 Simple RNN Elman Unit (pt 1) 00:09:09 Duration
Lecture 3 Simple RNN Elman Unit (pt 2) 00:09:31 Duration
Lecture 4 Aside State Space Models vs 00:03:20 Duration
Lecture 5 RNN Code Preparation 00:08:27 Duration
Lecture 6 RNNs Understanding by Implementing (Paying Attention to Shapes) 00:08:15 Duration
Lecture 7 GRU and LSTM (pt 1) 00:17:22 Duration
Lecture 8 GRU and LSTM (pt 2) 00:11:25 Duration
Lecture 9 LSTMs for Time Series Forecasting in Code 00:09:11 Duration
Lecture 10 LSTMs for Time Series Classification in Code 00:05:53 Duration
Lecture 11 The Unreasonable Ineffectiveness of Recurrent Neural Networks 00:03:07 Duration
Lecture 12 RNN Section Summary

Section 11 : VIP GARCH
Lecture 1 GARCH Section Introduction 00:03:45 Duration
Lecture 2 ARCH Theory (pt 1) 00:04:46 Duration
Lecture 3 ARCH Theory (pt 2) 00:07:26 Duration
Lecture 4 ARCH Theory (pt 3) 00:05:04 Duration
Lecture 5 GARCH Theory 00:07:30 Duration
Lecture 6 GARCH Code Preparation (pt 1) 00:07:43 Duration
Lecture 7 GARCH Code Preparation (pt 2) 00:07:46 Duration
Lecture 8 GARCH Code (pt 1) 00:05:56 Duration
Lecture 9 GARCH Code (pt 2) 00:08:19 Duration
Lecture 10 GARCH Code (pt 3) 00:07:00 Duration
Lecture 11 GARCH Code (pt 4) 00:05:41 Duration
Lecture 12 GARCH Code (pt 5) 00:04:09 Duration
Lecture 13 A Deep Learning Approach to GARCH 00:11:16 Duration
Lecture 14 GARCH Section Summary 00:06:25 Duration

Section 12 : VIP AWS Forecast
Lecture 1 AWS Forecast Section Introduction 00:07:51 Duration
Lecture 2 Data Model 00:09:06 Duration
Lecture 3 Creating an IAM Role 00:03:57 Duration
Lecture 4 Code pt 1 (Getting and Transforming the Data) 00:09:47 Duration
Lecture 5 Code pt 2 (Uploading the data to S3) 00:12:40 Duration
Lecture 6 Code pt 3 (Building your Model) 00:06:41 Duration
Lecture 7 Code pt 4 (Generating and Evaluating the Forecast) 00:06:39 Duration
Lecture 8 AWS Forecast Exercise 00:02:44 Duration
Lecture 9 AWS Forecast Section Summary 00:04:45 Duration

Section 13 : VIP Facebook Prophet
Lecture 1 Prophet Section Introduction 00:03:01 Duration
Lecture 2 How does Prophet work 00:08:15 Duration
Lecture 3 Prophet Code Preparation 00:12:31 Duration
Lecture 4 Prophet in Code Data Preparation 00:08:50 Duration
Lecture 5 Prophet in Code Fit, Forecast, Plot 00:08:19 Duration
Lecture 6 Prophet in Code Holidays and Exogenous Regressors 00:10:09 Duration
Lecture 7 Prophet in Code Cross-Validation 00:05:56 Duration
Lecture 8 Prophet in Code Changepoint Detection 00:04:04 Duration
Lecture 9 Prophet Multiplicative Seasonality, Outliers, Non-Daily Data 00:10:05 Duration
Lecture 10 (The Dangers of) Prophet for Stock Price Prediction 00:13:00 Duration
Lecture 11 Prophet Section Summary 00:03:17 Duration

Section 14 : Setting Up Your Environment FAQ
Lecture 1 Anaconda Environment Setup 00:20:13 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:11 Duration

Section 15 : Extra Help With Python Coding for Beginners FAQ
Lecture 1 How to Code by Yourself (part 1) 00:15:50 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:22 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:24 Duration

Section 16 : Effective Learning Strategies for Machine Learning FAQ
Lecture 1 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:21:57 Duration
Lecture 3 About Certification
Lecture 4 About Proctor Testing

Section 17 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:41 Duration