Python for ML Roadmap

You don’t need to master Python first and ML second. Learn the Python you need as you learn ML. This section covers the essential libraries.

Core Libraries

Data manipulation

• NumPy Essentials — arrays, vectorized operations, the foundation
• Pandas Essentials — DataFrames, loading/cleaning/transforming tabular data

Visualization

• Matplotlib Essentials — plots, charts, customization
• Seaborn Essentials — statistical plots, beautiful defaults

ML

• Scikit-Learn Overview — the Swiss army knife of classical ML

Deep Learning

• PyTorch Essentials — tensors, autograd, building neural nets

Environment setup

# create a virtual environment for ML work
python -m venv ~/Education/ai_ml/projects/.venv
source ~/Education/ai_ml/projects/.venv/bin/activate
 
# core stack
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
 
# deep learning (when you get there)
pip install torch torchvision torchaudio

Jupyter notebooks

Good for exploration and learning. Use ../projects/ for notebooks.

jupyter notebook --notebook-dir=~/Education/ai_ml/projects/

Links

• Math Roadmap — the math behind what these libraries compute
• Data Fundamentals Roadmap — using these tools on real data