The “where did that file go?” page

Sample notebooks

• Cell measurements intro for grouped summaries and plotting.
• Image QC exercises for simple image-like arrays and practice tasks.

Recommended course path

1. Install Python locally.
2. Create one environment with venv or conda.
3. Learn enough Python basics to read and edit beginner code.
4. Meet the main scientific Python libraries you will keep seeing.
5. Move into Jupyter notebooks and examples.
6. Use pandas and matplotlib for tables and plotting.
7. Install napari and try the napari-mAIcrobe plugin.
8. Create a small package once your workflow becomes reusable.
9. Continue into segmentation, classification, QC, neural networks, and pretrained models.

Recommended pacing

1. Day 1: install Python and get comfortable with the terminal.
2. Day 2: create and activate environments a few times.
3. Day 3: work slowly through Python basics and type the examples.
4. Day 4: meet NumPy, pandas, matplotlib, and the rest of the scientific stack.
5. Day 5: open Jupyter and complete the first notebook exercises.
6. Day 6: work through pandas and matplotlib with real tables and plots.
7. Day 7: install napari and explore one viewer-based workflow.
8. Day 8: scaffold a package and add one simple function.
9. Day 9 and beyond: move into segmentation, QC, classifiers, and pretrained models.

Useful repo pages

• Install Python
• Virtual environments
• Python basics
• Scientific Python libraries
• Jupyter notebooks
• pandas and matplotlib
• napari + mAIcrobe
• Create a package
• ML segmentation
• Single-cell classification
• Segmentation QC
• Neural networks
• Pretrained models and accessible DL
• Glossary
• Troubleshooting