IPN-Dharma IA Lab

    Bienvenidos
    IPN-Dharma IA Lab

    Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.

    Nivel 2: Conocimiento Contextual

    Hands-on Foundations for Data Science and Machine Learning with GC Labs

    In this program, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion. You will start learning the basics of BigQuery, building and optimizing warehouses, and then get hands-on practice on the more advanced data integration features available in Cloud Data Fusion.

    Learners will be able to practice:
    • Creating dataset partitions that will reduce cost and improve query performance.
    • Using macros in Data Fusion that introduce dynamic variables to plugin configurations so that you can specify the variable substitutions at runtime.
    • Building a reusable pipeline that reads data from Cloud Storage, performs data quality checks, and writes to Cloud Storage.
    • Using Google Cloud Machine Learning and TensorFlow to develop and evaluate prediction models using machine learning.
    • Implementing logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset.

    Cursos en este programa

    1) BigQuery Basics for Data Analysts

    Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive collection of BigQuery Google Cloud Labs Series. BigQuery is Google's fully managed, NoOps, low-cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

    Esfuerzo  Esfuerzo estimado 5 horas

    Idioma  Idioma inglés

    Link  Coursera

    2) Building Advanced Codeless Pipelines on Cloud Data Fusion

    In this Google Cloud Labs Series, learners get hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get to try out the data lineage feature as well to derive interesting insights into their data’s history.

    Esfuerzo  Esfuerzo estimado 6 horas

    Idioma  Idioma inglés

    Link  Coursera

    3) Data Science on Google Cloud

    Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Google Cloud Labs Series, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring, and visualizing data sets using Google Cloud tools and services.

    Esfuerzo  Esfuerzo estimado 11 horas

    Idioma  Idioma inglés

    Link  Coursera

    4) Data Science on Google Cloud: Machine Learning

    Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this Google Cloud Labs Series, covering chapter 9 through the end of the book, you run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

    Esfuerzo  Esfuerzo estimado 6 horas

    Idioma  Idioma inglés

    Link  Coursera

    © 2015 |Laboratorio de Microtecnología y Sistemas Embebidos | Centro de Investigación en Computación | Instituto Politécnico Nacional