This module introduces learners to the fundamentals of PyTorch, focusing on tensor operations, neural network construction, and training techniques. You will explore how to build and train feedforward, recurrent, and LSTM neural networks for tasks such as time series prediction. By the end, you'll gain hands-on experience with core deep learning workflows in PyTorch.

This module introduces key techniques for forecasting univariate time series, including classical ARIMA models and advanced deep learning approaches such as feedforward neural networks, LSTMs, and GRUs. Learners will gain practical skills in preparing time series data for supervised learning and handling challenges like trend, seasonality, and non-constant variance. By the end, you will be equipped to build and evaluate forecasting models tailored to univariate data.

This module guides learners through building and evaluating multivariate time series forecasting models using PyTorch Lightning. You will explore data preprocessing with TimeSeriesDataSet, implement linear regression, feedforward, and LSTM neural networks, and monitor training progress with TensorBoard. By the end, you'll be able to construct and assess forecasting models for complex time series data.

This module introduces advanced deep learning techniques for time series forecasting, focusing on multi-step and multi-output predictions using global LSTM models. Learners will discover how to prepare and train models on multiple time series, handle seasonal data, and optimize hyperparameters for improved forecasting accuracy.

This module delves into advanced deep learning models for time series forecasting, including DeepAR, Transformers, Temporal Fusion Transformers, and Informer architectures. Learners will gain hands-on experience with popular libraries such as GluonTS, NeuralForecast, and PyTorch Forecasting, and learn to optimize model performance through practical techniques like learning rate tuning.

This module introduces probabilistic approaches to time series forecasting, focusing on methods to quantify and interpret forecast uncertainty. Learners will explore techniques such as exceedance probability, prediction intervals, and advanced neural network models like LSTM and DeepAR, as well as Gaussian Processes. By the end, you'll be able to implement and evaluate probabilistic forecasts to support more informed decision-making.

This module introduces learners to deep learning techniques for classifying time series data, utilizing both PyTorch Lightning and the sktime library. You will gain hands-on experience building data pipelines, implementing convolutional neural networks and ResNets, and exploring alternative frameworks for time series classification tasks.

This module introduces deep learning techniques for detecting anomalies in time series data, focusing on models such as autoencoders, LSTMs, and VAEs. Learners will gain hands-on experience building and evaluating these models using Python libraries like PyOD. By the end, you'll understand how to leverage reconstruction and prediction errors to identify unusual patterns in real-world datasets.