Google Cloud Big Data and Machine Learning Fundamentals en Español

Go to Course: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals-es

Introduction

### Course Review: Google Cloud Big Data and Machine Learning Fundamentals en Español In today's data-driven world, mastering big data and machine learning is essential for professionals seeking to excel in any technology-related field. Coursera’s course titled **Google Cloud Big Data and Machine Learning Fundamentals en Español** is an excellent starting point for those looking to harness the power of Google Cloud in these domains. Here’s a detailed overview, review, and recommendation of the course. #### Overview The course provides learners with a comprehensive introduction to Google Cloud's big data and machine learning products and services. It covers the entire data lifecycle, from ingestion to transforming raw data into insightful information and eventually into intelligent predictions through AI. The course is in Spanish, making it an ideal choice for Spanish-speaking students and professionals. #### Syllabus Breakdown 1. **Introducción al curso** This section welcomes students to the course and outlines its goals and objectives, ensuring that learners know what to expect and how to navigate the material effectively. 2. **Macrodatos y aprendizaje automático en Google Cloud** Here, students delve into the essential components of Google Cloud infrastructure. They learn about the various products and services that play a role in transitioning from raw data to actionable AI insights. 3. **Ingeniería de datos para la transmisión de datos** This segment examines Google Cloud's solutions for managing streaming data. Learners explore an end-to-end data pipeline, including data transfer with Pub/Sub, data processing with Dataflow, and data visualization using Looker and Data Studio. 4. **Macrodatos con BigQuery** Students will gain insight into BigQuery—Google's fully managed, serverless data warehouse. This section also introduces BigQuery ML and the key commands and processes needed to create custom machine learning models. 5. **Opciones de aprendizaje automático en Google Cloud** This section outlines four distinct options available for creating machine learning models on Google Cloud, with a special focus on Vertex AI, which unifies the lifecycle of AI project management. 6. **Flujo de trabajo de aprendizaje automático con Vertex AI** Learners will engage with the three critical phases of the machine learning workflow in Vertex AI: data preparation, model training, and model deployment. Practical exercises include building a machine learning model with AutoML. 7. **Resumen del curso** The course concludes with a summary of the topics covered and offers additional resources for learners who wish to further explore big data and machine learning concepts beyond the course. #### Review The course is expertly structured and presents complex topics in a clear and accessible manner. Each module builds progressively on the previous one, allowing students to develop a robust understanding of both theoretical and practical aspects of big data and machine learning using Google Cloud. One of the standout features of this course is its hands-on approach. Students are not just passive learners; they actively engage with the tools and technologies that are central to modern data practices. For instance, the segment on BigQuery allows learners to work directly with a powerful data warehouse, providing a practical foundation that aligns well with real-world applications. Moreover, the inclusion of Vertex AI equips students with the latest tools for managing machine learning projects, reflecting current industry standards and practices. #### Recommendation I highly recommend **Google Cloud Big Data and Machine Learning Fundamentals en Español** for anyone looking to expand their knowledge and skills in big data and machine learning. Whether you're a beginner wanting to get started or an experienced professional looking to upskill, this course caters to a wide range of learners. The course’s comprehensive approach, combined with practical application and the ability to study in Spanish, makes it uniquely beneficial for Spanish-speaking individuals in the tech industry. In summary, if you are eager to make your mark in the fields of big data and machine learning, enrolling in this course is a step towards unlocking numerous opportunities in the world of data science and AI. Don’t miss out on this chance to elevate your expertise using Google Cloud!

Syllabus

Introducción al curso

En esta sección, se da la bienvenida al curso Big Data and Machine Learning Fundamentals y se proporciona una descripción general de la estructura y los objetivos del curso.

En esta sección, se exploran los componentes clave de la infraestructura de Google Cloud. Además, se presentan muchos de los productos y servicios de macrodatos y aprendizaje automático involucrados en el ciclo de vida de datos a IA en Google Cloud.

En esta sección, se presenta la solución de Google Cloud para administrar datos de transmisión. Además, se examina una canalización de extremo a extremo, lo que incluye la transferencia de datos con Pub/Sub, el procesamiento de datos con Dataflow y la visualización de datos con Looker y Data Studio.

En esta sección, los alumnos aprenderán sobre BigQuery, el almacén de datos sin servidores completamente administrado de Google. También explorarán BigQuery ML, así como los comandos clave y los procesos utilizados para crear modelos personalizados de aprendizaje automático.

En esta sección, se exploran las cuatro opciones diferentes para crear modelos de aprendizaje automático en Google Cloud. También se presenta Vertex AI, la plataforma unificada de Google para crear y administrar el ciclo de vida de los proyectos de AA.

Esta sección se enfoca en las tres fases clave del flujo de trabajo de aprendizaje automático en Vertex AI: preparación de datos, entrenamiento del modelo y preparación del modelo. Los alumnos tendrán la oportunidad de practicar la creación de un modelo de aprendizaje automático con AutoML.

En esta sección, se revisan los temas que se abordaron en el curso y se proporcionan recursos adicionales para un mayor aprendizaje.