Classical algorithms cannot automatically discover high-level features from raw data. For instance, in a housing price prediction model, the algorithm does not inherently know that "distance to the city center" is relevant unless the engineer creates that feature. Scikit-Learn shines in this phase through transformers like StandardScaler, OneHotEncoder, and custom Pipeline objects, ensuring reproducibility and preventing data leakage.
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
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El machine learning (aprendizaje automático) es una disciplina de la inteligencia artificial que permite a las máquinas aprender patrones a partir de datos y hacer predicciones o tomar decisiones sin programación explícita para cada caso. Tres herramientas clave en el ecosistema Python para aprender y aplicar machine learning son scikit‑learn, Keras y TensorFlow. Este ensayo presenta una visión estructurada y práctica para entender cuándo usar cada una, sus fortalezas, conceptos fundamentales, flujo de trabajo típico, ejemplos de aplicaciones y recomendaciones para avanzar.