💻 Software & Environment Setup
AI & Machine Learning in Healthcare
The computational toolchain and environment used throughout AI in Clinical Analytics.
Trial design training for researchersClinical data analysis workshopClinical trials training for researchersClinical trials workshopClinical analytics hands-on trainingClinical analytics training Germany
# 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 AI in Clinical Analytics
e-Certificate and e-Marksheet issued on successful completion.
