Artificial intelligence tools were used to support parts of the course creation process. The instructional design, technical workflow, engineering explanations, examples, and final course content were reviewed, refined, and shaped by the instructor. This course teaches firmware, embedded systems, and software engineers how to build a practical Edge AI workflow from raw time-series sensor data to a verified, deployment-ready model pipeline.
You will begin by learning how to prepare sensor data for machine learning. You will inspect time-series signals, identify missing samples, analyze ranges and drift, discover periodic patterns, and repair common data-quality problems. You will also learn why chronological training, validation, and test splits are essential for preventing data leakage. The course demonstrates how to normalize data correctly and convert continuous sensor readings into sliding windows that can be used for model training.
Next, you will build and train a compact Long Short-Term Memory (LSTM) model for sequence prediction. LSTM concepts such as memory, cell state, hidden state, and gates are explained in practical engineering terms. You will implement the model, create a training loop, select an optimizer and loss function, save the best checkpoint, and interpret training curves. Hands-on failure demonstrations will help you recognize overfitting, unstable training, inappropriate data shuffling, and other common problems.
