Machine learning and Deep Learning are powerful tools for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance.
However, developing predictive models for signals obtained from sensors is not a trivial task. Moreover, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes / embedded devices or on the cloud depending on the application.
In this session we will explore how you can use Signal Processing Toolbox and Wavelet Toolbox for analyzing real world signals. We will also explore how other addon tools like Statistics and Machine Learning Toolbox and Neural Network Toolbox can help for performing machine learning and deep learning.