💻 Software & Environment Setup
AI & Machine Learning in Healthcare
The computational toolchain and environment used throughout Machine Learning for Gas Sensors: Anomaly Detection and Domain-Aware Modeling.
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# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install torch torchvision scikit-learn monai pydicomPyTorch / TensorFlow
Deep-learning model training
Deep-learning model training
MONAI
Medical-imaging deep-learning framework
Medical-imaging deep-learning framework
scikit-learn
Classical ML, evaluation & pipelines
Classical ML, evaluation & pipelines
Weights & Biases
Experiment tracking & reproducibility
Experiment tracking & reproducibility
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Machine Learning for Gas Sensors: Anomaly Detection and Domain-Aware Modeling
e-Certificate and e-Marksheet issued on successful completion.
