💻 Software & Environment Setup
AI & Machine Learning in Healthcare
The computational toolchain and environment used throughout Machine Learning for IC Yield: Models, SHAP Explainability & APC/R2R.
Learn machine learning yieldMachine learning yield training course for researchersMachine learning yield workshop 2025Yield workshopMetrology workshopMachine learning yield industry applications
# 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 IC Yield: Models, SHAP Explainability & APC/R2R
e-Certificate and e-Marksheet issued on successful completion.
