Python for Machine Learning
Setup & Tools

Python for Machine Learning

Set up a clean Python environment for Machine Learning and get familiar with the core libraries you’ll use every day.

Environment Setup

  • Install Python 3.10+ from python.org or use Anaconda.
  • Use virtual environments (venv / conda) to isolate project dependencies.
  • Keep requirements in a requirements.txt file or environment.yml.
Creating a virtual environment (venv)
python -m venv .venv
source .venv/bin/activate   # Linux / macOS
.venv\Scripts\activate      # Windows

Core Python ML Libraries

  • NumPy: n‑dimensional arrays, linear algebra and numerical operations.
  • Pandas: tabular data structures (DataFrame) and data manipulation.
  • Matplotlib / Seaborn: data visualization.
  • scikit‑learn: ML models, preprocessing, metrics and pipelines.

Typical Project Structure

  • data/: raw and processed datasets.
  • notebooks/: exploratory Jupyter notebooks.
  • src/: reusable Python modules for preprocessing, models and utilities.
  • tests/: basic unit tests for critical logic.
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