René's URL Explorer Experiment

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  "@type": "CollectionPage",
  "@id": "https://realpython.com/tutorials/machine-learning/",
  "url": "https://realpython.com/tutorials/machine-learning/",
  "name": "Python Machine Learning",
  "description": "Learn how to implement machine learning (ML) algorithms in Python. With these skills, you can create intelligent systems capable of learning and making decisions.",
  "image": {
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    "url": "https://files.realpython.com/media/Python-Machine-Learning_Watermarked.c9684236cea2.jpg",
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    "height": 1080
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  "publisher": {
    "@type": "Organization",
    "@id": "https://realpython.com/#organization",
    "name": "Real Python",
    "url": "https://realpython.com",
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    "slogan": "Become a Python Expert",
    "email": "info@realpython.com",
    "sameAs": [
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      "https://www.youtube.com/realpython",
      "https://twitter.com/realpython",
      "https://x.com/realpython",
      "https://www.linkedin.com/company/realpython-com/",
      "https://www.facebook.com/learnrealpython",
      "https://www.instagram.com/realpython",
      "https://www.tiktok.com/@realpython.com"
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  "about": {
    "@type": "Thing",
    "name": "Python Machine Learning",
    "description": "Learn how to implement machine learning (ML) algorithms in Python. With these skills, you can create intelligent systems capable of learning and making decisions."
  },
  "hasPart": {
    "@type": "FAQPage",
    "mainEntity": [
      {
        "@type": "Question",
        "name": "How do I install Python machine learning libraries?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "
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.
"
        }
      },
      {
        "@type": "Question",
        "name": "Which Python library should I choose for machine learning?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "
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.
"
        }
      },
      {
        "@type": "Question",
        "name": "How do I train a classifier in scikit-learn?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "
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.
"
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        "@type": "Question",
        "name": "How do I deploy a TensorFlow or PyTorch model with FastAPI?",
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          "@type": "Answer",
          "text": "
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.
"
        }
      },
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        "@type": "Question",
        "name": "How can I speed up model training in Python?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "
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.
"
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