Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting
InstructorLazy Programmer Team
Instruction covers a wide range of methods, from Exponential Smoothing and ARIMA to advanced Recurrent Neural Networks (RNN) and LSTMs.
The presentation of algorithm mechanisms and prerequisites is noted as logical, using geometric descriptions to clarify classification and regression tasks.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the practical benefits of the course, highlighting skills in forecasting sales and biological data alongside classifying user activity and brain signals.
This slide outlines exclusive VIP course content, highlighting advanced tools such as AWS Forecast, GARCH volatility modeling, and the Facebook Prophet library.
This slide introduces the target audience by highlighting the importance of forecasting in public policy, illustrated by a time series chart tracking pandemic-related data.
One sampled review suggests that learners have reported difficulties navigating external links to find datasets and issues downloading notebooks in usable formats. Note that these signals predate the displayed update date, which does not prove a correction has occurred.
Some signals suggest that code explanations can be rapid, potentially requiring more in-depth technical detail for implementation. Note that these signals predate the displayed update date, which does not prove a correction has occurred.
The curriculum aligns with the stated goals for data science and finance professionals, including necessary Python foundations.