Introduction
- Course Overview - PLEASE DO NOT SKIP THIS LECTURE
- Course Overview Check
- Course Curriculum Overview
- FAQ - Frequently Asked Questions
Course Set Up and Install
- Installing Anaconda Python Distribution and Jupyter
NumPy
- NumPy Section Overview
- NumPy Arrays - Part One
- NumPy Arrays - Part Two
- NumPy Indexing and Selection
- NumPy Operations
- NumPy Exercises
- NumPy Exercise Solutions
Pandas Overview
- Introduction to Pandas
- Series
- DataFrames - Part One
- DataFrames - Part Two
- Missing Data with Pandas
- Group By Operations
- Common Operations
- Data Input and Output
- Pandas Exercises
- Pandas Exercises Solutions
Data Visualization with Pandas
- Overview of Capabilities of Data Visualization with Pandas
- Visualizing Data with Pandas
- Customizing Plots created with Pandas
- Pandas Data Visualization Exercise
- Pandas Data Visualization Exercise Solutions
Time Series with Pandas
- Overview of Time Series with Pandas
- DateTime Index
- DateTime Index Part Two
- Time Resampling
- Time Shifting
- Rolling and Expanding
- Visualizing Time Series Data
- Visualizing Time Series Data - Part Two
- Time Series Exercises - Set One
- Time Series Exercises - Set One - Solutions
- Time Series with Pandas Project Exercise - Set Two
- Time Series with Pandas Project Exercise - Set Two - Solutions
Time Series Analysis with Statsmodels
- Introduction to Time Series Analysis with Statsmodels
- Introduction to Statsmodels Library
- ETS Decomposition
- EWMA - Theory
- EWMA - Exponentially Weighted Moving Average
- Holt - Winters Methods Theory
- Holt - Winters Methods Code Along - Part One
- Holt - Winters Methods Code Along - Part Two
- Statsmodels Time Series Exercises
- Statsmodels Time Series Exercise Solutions
General Forecasting Models
- Introduction to General Forecasting Section
- Introduction to Forecasting Models Part One
- Evaluating Forecast Predictions
- Introduction to Forecasting Models Part Two
- ACF and PACF Theory
- ACF and PACF Code Along
- ARIMA Overview
- Autoregression - AR - Overview
- Autoregression - AR with Statsmodels
- Descriptive Statistics and Tests - Part One
- Descriptive Statistics and Tests - Part Two
- Descriptive Statistics and Tests - Part Three
- ARIMA Theory Overview
- Choosing ARIMA Orders - Part One
- Choosing ARIMA Orders - Part Two
- ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One
- ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two
- SARIMA - Seasonal Autoregressive Integrated Moving Average
- SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE
- SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO
- SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3
- Vector AutoRegression - VAR
- VAR - Code Along
- VAR - Code Along - Part Two
- Vector AutoRegression Moving Average - VARMA
- Vector AutoRegression Moving Average - VARMA - Code Along
- Forecasting Exercises
- Forecasting Exercises - Solutions
Deep Learning for Time Series Forecasting
- Introduction to Deep Learning Section
- Perceptron Model
- Introduction to Neural Networks
- Keras Basics
- Recurrent Neural Network Overview
- LSTMS and GRU
- Keras and RNN Project - Part One
- Keras and RNN Project - Part Two
- Keras and RNN Project - Part Three
- Keras and RNN Exercise
- Keras and RNN Exercise Solutions
- BONUS: Multivariate Time Series with RNN
- Quick Check on MultiVariate Time Series Notebook and Data
- BONUS: Multivariate Time Series with RNN
