Course description

Time series data (for example, closing prices of an exchange-traded fund, maximum yearly temperatures, monthly PC sales, or daily numbers of visitors) arise whenever correlations of adjacent observations in time cannot be ignored. This course covers modern methods for time series analysis and forecasting. In addition to mathematical foundations of time series, students get hands-on experience building predictive models in cases of both stationary and non-stationary time series. Topics covered in the course include autocorrelation and partial autocorrelation, Fourier analysis, stationarity, time series decomposition, autoregressive integrated moving average (ARIMA) process and the Box-Jenkins methodology, generalized autoregressive conditional heteroskedasticity (GARCH) model, and long short-term memory (LSTM), a special type of recurrent neural networks (RNN) which has demonstrated to be superior to classical time series models in many applications.

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Online

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.

Price
Free*
Duration
7 weeks long
Registration Deadline
Available now