Python Machine Learning Tutorials
You want to build real machine learning systems in Python. These tutorials help you prep data with pandas and NumPy, train models with scikit-learn, TensorFlow, and PyTorch, and tackle computer vision with OpenCV and speech recognition tasks.
Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.
You practice core workflows like feature engineering, cross-validation, evaluation metrics, and pipelines so your models generalize. Use embeddings and transformers with a vector store to build RAG and search.
Browse all resources below, or commit to a guided Learning Path with progress tracking:
Learning Path
Machine Learning With Python
26 Resources ⋅ Skills: Image Processing, Text Classification, Speech Recognition
Learning Path
Math for Data Science
5 Resources ⋅ Skills: Statistics, Correlation, Linear Regression, Logistic Regression
Create a virtual environment, then run python -m pip install numpy pandas scikit-learn torch tensorflow opencv-python. On Apple Silicon, use tensorflow-macos and tensorflow-metal for GPU.
Use scikit-learn for classic ML on tabular data. Choose TensorFlow or PyTorch for deep learning. Pick PyTorch for flexible research and TensorFlow for Keras APIs and TPU/mobile options.
Load a DataFrame, train_test_split, and build a Pipeline with preprocessing and an estimator like LogisticRegression(). Fit with .fit(X_train, y_train) and evaluate with cross_val_score or classification_report.
Save the model with model.save() or torch.save(). In FastAPI, load it at startup, expose a POST /predict with pydantic validation, and return JSON. Run with uvicorn, then containerize with Docker for production.
Use vectorized NumPy code and n_jobs=-1 for parallel scikit-learn estimators. For deep learning, enable GPU, mixed precision, and efficient data loaders. Cache features, profile bottlenecks, and stream data to avoid memory spikes.
