Shallow Neural Networks for Time Series Forecasting

1. For best price copy paste this code at checkout, after removing the space in the middle: 4C1C37AD341 A1DE9B5E02. The course gets updated every 6-12 months.

Visit often to download new material. 3. Course Overview: Shallow neural networks, consisting of just one hidden layer, are capable of modeling non-linear relationships effectively in tasks where data is limited and interpretability is important.

They are best suited for regression, binary classification, and simple function approximation, offering faster training and lower risk of overfitting compared to deeper architectures. While they may struggle with highly complex patterns like those in image recognition or natural language processing, they perform well in structured data and control or optimization applications. The course also introduces time series forecasting techniques with a focus on CO₂ emissions modeling using Python.

Learners will explore concepts like stationarity, differencing, and autocorrelation, applying them through hands-on exercises using real-world CO₂ datasets. Python libraries such as pandas, statsmodels, and matplotlib are used for building and visualizing forecasting models. Downloadable code, Jupyter notebooks, and instructor support are provided to ensure practical skill development.