Section 1 : Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Certification
Lecture 3 Course Curriculum Overview 00:04:02 Duration
Lecture 4 About Proctor Testing

Section 2 : Course Set Up and Install
Lecture 1 Installing Anaconda Python Distribution and Jupyter 00:15:48 Duration

Section 3 : NumPy
Lecture 1 NumPy Section Overview 00:00:38 Duration
Lecture 2 NumPy Arrays - Part One 00:10:40 Duration
Lecture 3 NumPy Arrays - Part Two 00:08:04 Duration
Lecture 4 NumPy Indexing and Selection 00:12:11 Duration
Lecture 5 NumPy Operations 00:06:40 Duration
Lecture 6 NumPy Exercises 00:01:13 Duration
Lecture 7 NumPy Exercise Solutions 00:07:00 Duration

Section 4 : Pandas Overview
Lecture 1 Introduction to Pandas 00:01:04 Duration
Lecture 2 Series 00:09:55 Duration
Lecture 3 DataFrames - Part One 00:13:18 Duration
Lecture 4 DataFrames - Part Two 00:11:03 Duration
Lecture 5 Missing Data with Pandas 00:08:21 Duration
Lecture 6 Group By Operations 00:05:37 Duration
Lecture 7 Common Operations 00:09:15 Duration
Lecture 8 Data Input and Output 00:10:12 Duration
Lecture 9 Pandas Exercises 00:03:00 Duration
Lecture 10 Pandas Exercises Solutions 00:13:52 Duration

Section 5 : Data Visualization with Pandas
Lecture 1 Overview of Capabilities of Data Visualization with Pandas 00:01:35 Duration
Lecture 2 Visualizing Data with Pandas 00:19:18 Duration
Lecture 3 Customizing Plots created with Pandas
Lecture 4 Pandas Data Visualization Exercise 00:03:24 Duration
Lecture 5 Pandas Data Visualization Exercise Solutions 00:07:25 Duration

Section 6 : Time Series with Pandas
Lecture 1 Overview of Time Series with Pandas 00:01:04 Duration
Lecture 2 DateTime Index 00:10:15 Duration
Lecture 3 DateTime Index Part Two 00:11:42 Duration
Lecture 4 Time Resampling 00:12:05 Duration
Lecture 5 Time Shifting 00:05:30 Duration
Lecture 6 Rolling and Expanding 00:09:33 Duration
Lecture 7 Visualizing Time Series Data
Lecture 8 Visualizing Time Series Data - Part Two 00:13:03 Duration
Lecture 9 Time Series Exercises - Set One 00:01:18 Duration
Lecture 10 Time Series Exercises - Set One - Solutions 00:04:33 Duration
Lecture 11 Time Series with Pandas Project Exercise - Set Two
Lecture 12 Time Series with Pandas Project Exercise - Set Two - Solutions 00:15:14 Duration

Section 7 : Time Series Analysis with Statsmodels
Lecture 1 Introduction to Time Series Analysis with Statsmodels 00:01:14 Duration
Lecture 2 Introduction to Statsmodels Library 00:13:12 Duration
Lecture 3 ETS Decomposition 00:10:20 Duration
Lecture 4 EWMA - Theory 00:04:27 Duration
Lecture 5 EWMA - Exponentially Weighted Moving Average 00:14:01 Duration
Lecture 6 Holt - Winters Methods Theory 00:09:38 Duration
Lecture 7 Holt - Winters Methods Code Along - Part One 00:10:21 Duration
Lecture 8 Holt - Winters Methods Code Along - Part Two 00:15:40 Duration
Lecture 9 Statsmodels Time Series Exercises 00:02:38 Duration
Lecture 10 Statsmodels Time Series Exercise Solutions 00:06:13 Duration

Section 8 : General Forecasting Models
Lecture 1 Introduction to General Forecasting Section 00:03:36 Duration
Lecture 2 Introduction to Forecasting Models Part One 00:13:14 Duration
Lecture 3 Evaluating Forecast Predictions 00:08:55 Duration
Lecture 4 Introduction to Forecasting Models Part Two 00:11:13 Duration
Lecture 5 ACF and PACF Theory 00:10:11 Duration
Lecture 6 ACF and PACF Code Along 00:16:49 Duration
Lecture 7 ARIMA Overview 00:13:45 Duration
Lecture 8 Autoregression - AR - Overview 00:05:53 Duration
Lecture 9 Autoregression - AR with Statsmodels 00:26:37 Duration
Lecture 10 Descriptive Statistics and Tests - Part One 00:08:21 Duration
Lecture 11 Descriptive Statistics and Tests - Part Two 00:20:39 Duration
Lecture 12 Descriptive Statistics and Tests - Part Three 00:07:23 Duration
Lecture 13 ARIMA Theory Overview 00:06:07 Duration
Lecture 14 Choosing ARIMA Orders - Part One 00:06:31 Duration
Lecture 15 Choosing ARIMA Orders - Part Two 00:13:54 Duration
Lecture 16 ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One 00:12:25 Duration
Lecture 17 ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two 00:26:46 Duration
Lecture 18 SARIMA - Seasonal Autoregressive Integrated Moving Average 00:17:45 Duration
Lecture 19 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE 00:07:24 Duration
Lecture 20 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO 00:22:02 Duration
Lecture 21 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3 00:20:32 Duration
Lecture 22 Vector AutoRegression - VAR 00:05:52 Duration
Lecture 23 VAR - Code Along
Lecture 24 VAR - Code Along - Part Two 00:15:43 Duration
Lecture 25 Vector AutoRegression Moving Average - VARMA 00:02:51 Duration
Lecture 26 Vector AutoRegression Moving Average - VARMA - Code Along 00:09:20 Duration
Lecture 27 Forecasting Exercises 00:02:02 Duration
Lecture 28 Forecasting Exercises - Solutions 00:08:55 Duration

Section 9 : Deep Learning for Time Series Forecasting
Lecture 1 Introduction to Deep Learning Section 00:04:24 Duration
Lecture 2 Perceptron Model 00:05:06 Duration
Lecture 3 Introduction to Neural Networks 00:06:29 Duration
Lecture 4 Keras Basics 00:15:21 Duration
Lecture 5 Recurrent Neural Network Overview 00:07:41 Duration
Lecture 6 LSTMS and GRU 00:10:06 Duration
Lecture 7 Keras and RNN Project - Part One 00:12:05 Duration
Lecture 8 Keras and RNN Project - Part Two 00:11:04 Duration
Lecture 9 Keras and RNN Project - Part Three 00:25:18 Duration
Lecture 10 Keras and RNN Exercise
Lecture 11 Keras and RNN Exercise Solutions 00:13:17 Duration

Section 10 : Facebook's Prophet Library
Lecture 1 Overview of Facebook's Prophet Library 00:03:16 Duration
Lecture 2 Facebook's Prophet Library 00:16:30 Duration
Lecture 3 Facebook Prophet Evaluation 00:16:08 Duration
Lecture 4 Facebook Prophet Trend 00:04:32 Duration
Lecture 5 Facebook Prophet Seasonality 00:05:31 Duration