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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.orgor use Anaconda. - Use virtual environments (
venv/conda) to isolate project dependencies. - Keep requirements in a
requirements.txtfile orenvironment.yml.
Creating a virtual environment (venv)
python -m venv .venv
source .venv/bin/activate # Linux / macOS
.venv\Scripts\activate # WindowsCore 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.