IoT Data Analytics and Machine Learning
Description
Advanced data analytics and machine learning techniques for IoT applications
Learning Objectives
Learners will master IoT data analytics including real-time stream processing, time-series analysis, predictive maintenance, and machine learning model deployment. They will understand data preprocessing, feature engineering, model selection, and implementation of AI-driven IoT solutions for various industry applications.
Topics (8)
Specialized analytics for equipment monitoring, failure prediction, and anomaly detection
Model deployment strategies, version control, monitoring, and MLOps practices for IoT systems
Study of IoT data characteristics including volume, velocity, variety, and preprocessing methods
Implementation of real-time stream processing for IoT data including windowing and aggregation
Advanced time-series analysis including trend detection, seasonality, and forecasting algorithms
Implementation of ML algorithms for IoT including classification, regression, and clustering
Application of CNNs, RNNs, and other neural network architectures to IoT data
Design and implementation of IoT dashboards using modern visualization tools and techniques