Programa Especializado - Aprendizaje Automático

Comenzó el mar. 20

Programa Especializado - Aprendizaje Automático

Build Intelligent Applications

Master machine learning fundamentals in five hands-on courses.

Sobre este Programa Especializado

This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, you’ll apply your skills to solve an original, real-world problem through implementation of machine learning algorithms.

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courses
4 courses

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Proyectos

Diseñado para ayudarte a practicar y aplicar las habilidades que aprendiste.

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Certificados

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Descripción general de los proyectos

Cursos
Intermediate Specialization.
Some related experience required.
  1. CURSO 1

    Fundamentos del Aprendizaje Automático: Planteamiento de un Caso de Estudio

    Sesión actual: mar. 20 — may. 8.
    Dedicación
    6 semanas de estudio, 5-8 horas/semana
    Subtítulos
    English, Korean, Chinese (Simplified)

    Acerca del Curso

    ¿Tiene datos y se pregunta qué puede decir a partir de ellos? ¿Necesita una comprensión más profunda de las formas principales en que el aprendizaje automático pueden mejorar su negocio? ¿Quieres ser capaz de conversar con especialistas sobre cualquier cosa, desde la regresión y clasificación, de aprendizaje profundo y de sistemas de recomendación? En este curso, obtendrá la experiencia práctica con el aprendizaje automático en una serie de estudios de casos prácticos. Al final del primer curso habrás estudiado cómo predecir precios de la vivienda en base a niveles de características , analizar el sentimiento de los comentarios de los usuarios, recuperar documentos de interés, recomendación de productos, y la búsqueda de imágenes. A través de ejercicios prácticos con estos casos de uso, usted será capaz de aplicar métodos de aprendizaje automático en una amplia gama de dominios. Este primer curso trata el método de aprendizaje automático como una caja negra. El uso de esta abstracción, que se centrará en la comprensión de las tareas de interés, haciendo coincidir estas tareas a las herramientas de aprendizaje automático, y la evaluación de la calidad de la salida. En cursos posteriores, usted profundizar en los componentes de este cuadro negro mediante el examen de los modelos y algoritmos. En conjunto, estas piezas forman la tubería de aprendizaje automático, que va a utilizar en el desarrollo de aplicaciones inteligentes. Resultados del aprendizaje: Al final de este curso, usted será capaz de:     - Identificar potenciales aplicaciones de aprendizaje automático en la práctica.     - Describir Las diferencias fundamentales en los análisis por regresión, clasificación y agrupamiento.     -Seleccionar La tarea de aprendizaje automático apropiado para una potencial aplicación.     -Aplicar la regresión, clasificación, agrupamiento, recuperación, los sistemas de recomendación, y el aprendizaje profundo.     - Representaría sus datos como características para servir como entrada a los modelos de aprendizaje automático.     -Evaluar la calidad del modelo en términos de indicadores de error relevantes para cada tarea.     -Utilizar un conjunto de datos para ajustarse a un modelo para analizar los nuevos datos.     -Construir Una aplicación de extremo a extremo que utiliza el aprendizaje automático en su núcleo.     -Implementar estas técnicas en Python.
  2. CURSO 2

    Regresión

    Sesión actual: mar. 20 — may. 8.
    Dedicación
    6 weeks of study, 5-8 hours/week
    Subtítulos
    English

    Acerca del Curso

    Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.
  3. CURSO 3

    Clasificación

    Sesión actual: mar. 20 — may. 15.
    Dedicación
    7 weeks of study, 5-8 hours/week
    Subtítulos
    English

    Acerca del Curso

    Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).
  4. CURSO 4

    Clustering y Recuperación

    Sesión actual: mar. 20 — may. 8.
    Dedicación
    6 weeks of study, 5-8 hours/week
    Subtítulos
    English

    Acerca del Curso

    Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.

Creadores

  • Universidad de Washington

    The University of Washington is a national and international leader in the core fields that are driving data science: computer science, statistics, human-centered design, and applied math.

    Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.

  • Emily Fox

    Emily Fox

    Amazon Professor of Machine Learning
  • Carlos Guestrin

    Carlos Guestrin

    Amazon Professor of Machine Learning

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