📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI-Powered Biosignal Analytics and Remote Patient Monitoring Hands-on Bootcamp, from foundations to a certified capstone project.
Outline
Design and implement neural network architectures for biosignal processing using Python and TensorFlow • Analyze and visualize biosignal data using matplotlib and scikit-learn to identify patterns and trends • Develop and evaluate machine learning models for biosignal classification using cross-validation and metrics such as accuracy and F1-score
Outline
Configure and optimize data pipelines for biosignal data using Apache Beam and Google Cloud Dataflow • Implement data preprocessing techniques such as filtering, normalization, and feature extraction using Python and Pandas • Evaluate and compare the performance of different feature engineering techniques using metrics such as mean squared error and R-squared
Outline
Develop and train deep learning models for biosignal analysis using Keras and TensorFlow • Design and evaluate algorithmic approaches for biosignal processing such as wavelet transforms and Fourier analysis • Implement and compare the performance of different machine learning algorithms for biosignal classification using metrics such as precision and recall
Outline
Implement hyperparameter tuning using grid search and random search for machine learning models • Evaluate and compare the performance of different machine learning models using metrics such as mean absolute error and coefficient of determination • Develop and implement early stopping and learning rate scheduling techniques for training deep learning models
Outline
Configure and deploy machine learning models using Docker and Kubernetes • Implement and manage production workflows for biosignal analytics using Apache Airflow and Zapier • Develop and evaluate monitoring and logging strategies for machine learning models in production using Prometheus and Grafana
Outline
Analyze and evaluate the ethical implications of AI-powered biosignal analytics using case studies and scenarios • Develop and implement strategies for bias mitigation and fairness in machine learning models using techniques such as data preprocessing and regularization • Design and evaluate approaches for transparency and explainability in AI-powered biosignal analytics using techniques such as feature importance and partial dependence plots
Outline
Develop and evaluate business cases for AI-powered biosignal analytics in healthcare and medical devices • Implement and integrate AI-powered biosignal analytics with existing healthcare systems and infrastructure • Analyze and compare the performance of different AI-powered biosignal analytics solutions using case studies and benchmarks
Earn government-registered certification in AI-Powered Biosignal Analytics and Remote Patient Monitoring Hands-on Bootcamp
e-Certificate and e-Marksheet issued on successful completion.
