Descripción:
Si la NLP no ha sido su fuerte, este curso le asegurarán que comience de manera estable. Este curso le mostrará cómo usar efectivamente las bibliotecas de Python y los conceptos de NLP para resolver varios problemas. Se le presentará el procesamiento del lenguaje natural y sus aplicaciones a través de ejemplos y ejercicios. Esto será seguido por una introducción a las etapas iniciales de resolución de un problema, que incluye la definición del problema, la obtención de datos de texto y su preparación para el modelado. Con la exposición a conceptos como algoritmos avanzados de procesamiento de lenguaje natural y técnicas de visualización, aprenderá cómo crear aplicaciones que puedan extraer información de datos no estructurados y presentarla como imágenes impactantes. Aunque continuará aprendiendo técnicas basadas en NLP, el enfoque cambiará gradualmente a desarrollar aplicaciones útiles. En estas secciones, comprenderá cómo aplicar técnicas de NLP para responder preguntas como se puede usar en chatbots.
Objetivo:
Al final de este curso, podrá realizar una amplia gama de tareas que van desde identificar el tipo de tarea de NLP más adecuado para resolver un problema hasta usar una herramienta como spacy o gensim para realizar análisis de sentimientos. El curso lo equipará fácilmente con el conocimiento que necesita para crear aplicaciones que interpreten el lenguaje humano. Este curso requiere que el alumno cuente con conocimientos sólidos de programación con Python.
Temario:
Parte 1. Introducción al procesamiento del lenguaje natural
Introducción | Historia de la PNL | Análisis de texto y PNL | Varios pasos en PNL | Iniciar un proyecto de PNL
Parte 2. Métodos básicos de extracción de características
Tipos de datos | Limpieza de datos de texto | Extracción de funciones de textos | Ingeniería de características
Parte 3. Desarrollando un clasificador de texto
Aprendizaje automático | Desarrollar un clasificador de texto | Construcción de tuberías para proyectos de PNL | Guardar y cargar modelos
Parte 4. Recopilar datos de texto de la web
Recopilación de datos raspando páginas web | Solicitud de contenido de páginas web | Manejo de datos semiestructurados |
Parte 5. Modelado de temas
Descubrimiento del tema | Algoritmos de modelado de temas
Parte 6. Resumen de texto y generación de texto
¿Qué es el resumen automático de texto? El | Vista de alto nivel de resumen de texto | TextRank | Resumen de texto con Gensim | Resumen de texto usando la frecuencia de palabras | Generando texto con cadenas de Markov
Parte 7. Representación vectorial
Definición vectorial | ¿Por qué representaciones vectoriales?
Parte 8. Análisis de sentimientos
¿Por qué se requiere el análisis de sentimientos? El | Análisis del crecimiento del sentimiento | Herramientas utilizadas para el análisis de sentimientos | TextBlob | Comprensión de los datos para el análisis de sentimientos | Modelos de sentimiento de entrenamiento
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