Home > Science & Mathematics > Mathematics > Probability & statistics > Ciencia de Datos a Través de Python. Técnicas de Aprendizaje Supervisado: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES
Ciencia de Datos a Través de Python. Técnicas de Aprendizaje Supervisado: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES

Ciencia de Datos a Través de Python. Técnicas de Aprendizaje Supervisado: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES

          
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About the Book


La ciencia de datos es un campo interdisciplinario que utiliza métodos, algoritmos, procesos y sistemas para extraer conocimiento y conclusiones de todo tipo de datos. A través del aprendizaje automático, combina elementos de estadística, informática, matemáticas y técnicas de análisis para resolver problemas, hacer predicciones y generar valor a partir de los datos. Se basa en grandes volúmenes de datos (big data) para descubrir patrones, tendencias y relaciones que puedan utilizarse para la toma de decisiones. El aprendizaje automático utiliza dos tipos de técnicas: el aprendizaje supervisado, que entrena un modelo con datos de entrada y salida conocidos para que pueda predecir resultados futuros, y el aprendizaje no supervisado, que encuentra patrones ocultos o estructuras intrínsecas en los datos de entrada. El aprendizaje supervisado utiliza técnicas de clasificación y regresión basadas en modelos predictivos, dependiendo de la naturaleza de la variable dependiente. Si es categórica, estamos ante técnicas de clasificación predictiva, y si es cuantitativa, estamos ante técnicas de regresión predictiva. En este libro se desarrollan técnicas de aprendizaje supervisado, profundizando en clasificadores como el algoritmo KNN (Nearest Neighbor), el algoritmo SVM (Support Vector Machine) y el algoritmo Naive Bayes. A continuación, se abordan técnicas de ensamblaje de modelos como Boosting, Bagging, Stacking, Voting y Blending. Finalmente, se desarrollan temas avanzados como modelos de redes neuronales para clasificación. Se tienen en cuenta arquitecturas como el Perceptrón Multicapa, la Red de Base Radial, Redes Adaline, Redes Hopfield y redes neuronales para predicción de series temporales (redes LSTM, redes recurrentes RNN, redes GRU y redes NARX). Para todos los temas, se presentan conceptos metodológicos e ilustran con ejemplos prácticos y ejercicios totalmente resueltos en código Python.


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Product Details
  • ISBN-13: 9798342271011
  • Publisher: Amazon Digital Services LLC - Kdp
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 190
  • Spine Width: 10 mm
  • Weight: 341 gr
  • ISBN-10: 8342271013
  • Publisher Date: 08 Oct 2024
  • Binding: Paperback
  • Language: Spanish
  • Returnable: N
  • Sub Title: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES
  • Width: 178 mm


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Ciencia de Datos a Través de Python. Técnicas de Aprendizaje Supervisado: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES
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Ciencia de Datos a Través de Python. Técnicas de Aprendizaje Supervisado: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING Y REDES NEURONALES
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