Serverless Machine Learning con TensorFlow en GCP from Coursera

En este curso acelerado a pedido de una semana, los participantes recibirán una introducción práctica sobre cómo diseñar y compilar modelos de aprendizaje automático en Google Cloud Platform. Mediante una serie de presentaciones, demostraciones y labs prácticos, los participantes conocerán conceptos de aprendizaje automático (AA) y TensorFlow, y adquirirán habilidades prácticas para desarrollar, evaluar y producir modelos de AA. OBJETIVOS En este curso, los participantes adquirirán las siguientes habilidades: ● Identificar casos prácticos de aprendizaje automático ● Compilar un modelo de AA con TensorFlow ● Compilar modelos de AA implementables y escalables con Cloud ML ● Conocer la importancia del procesamiento previo y la combinación de atributos ● Incorporar conceptos avanzados de AA a sus modelos ● Llevar modelos entrenados de AA a producción REQUISITOS PREVIOS Para aprovechar al máximo este curso, los participantes deben cumplir con los siguientes requisitos previos: ● Haber completado el curso "Google Cloud Fundamentals - Big Data and Machine Learning" O contar con experiencia equivalente ● Tener un conocimiento básico del lenguaje de consulta común, como SQL ● Tener experiencia con las actividades de extracción, transformación, carga y modelado de datos ● Haber desarrollado aplicaciones mediante un lenguaje de programación común, como Python ● Estar familiarizados con el aprendizaje automático o las estadísticas Notas sobre la Cuenta de Google: • Por el momento, los servicios de Google no están disponibles en China.

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Develops practical skills in machine learning model design and implementation using TensorFlow and Google Cloud Platform, which are core skills in data science and machine learning

Covers industry-standard machine learning tools and concepts, including TensorFlow and Google Cloud Platform, preparing learners for real-world applications

Taught by instructors from Google Cloud Training, recognized for their expertise in cloud computing and machine learning

Provides hands-on labs and practical exercises, allowing learners to apply their knowledge and gain experience in building machine learning models

Emphasizes the importance of data preprocessing and feature engineering, which are crucial for effective machine learning model development

Reviews summary

Theoretical, practical tensorflow

This course offers a good balance of theoretical content with lots of practical work so that learners are sure to understand the concepts.

Balanced Theory and Practical Exercises

"Muy sintético en la teoría y con ejercicios prácticos suficientes para comprender los temas."

Not the Fourth Course?

"este curso deberia ser el cuarto por lo que dice el presentador"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Serverless Machine Learning con TensorFlow en GCP with these activities:

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

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Read this book to enhance your understanding of machine learning concepts and techniques used in this course.

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Read the relevant chapters

Practice TensorFlow exercises

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Practice using TensorFlow to develop and train machine learning models.

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Complete the TensorFlow tutorials
Work through the TensorFlow exercises

Practice data preprocessing and feature engineering

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Practice data preprocessing and feature engineering to improve the accuracy of your machine learning models.

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Complete the data preprocessing and feature engineering exercises

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Practice using advanced machine learning algorithms

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Practice using advanced machine learning algorithms, such as support vector machines and decision trees, to enhance the accuracy of your models.

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Complete the advanced machine learning algorithms exercises

Create a machine learning model with Cloud ML

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Follow tutorials to build and deploy a machine learning model using Cloud ML.

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Follow the Cloud ML tutorial
Deploy your model to the cloud

Create a data pipeline using Google Cloud Dataflow

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Follow tutorials to create a data pipeline using Google Cloud Dataflow to process and transform data for machine learning.

Browse courses on Google Cloud Dataflow

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Follow the Google Cloud Dataflow tutorial
Deploy your data pipeline to the cloud

Use Google Cloud AI Platform Notebooks

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Follow tutorials to use Google Cloud AI Platform Notebooks to develop and deploy machine learning models.

Browse courses on Jupyter Notebooks

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Follow the Google Cloud AI Platform Notebooks tutorial
Deploy your machine learning model

Develop a machine learning project

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Apply the concepts and techniques learned in the course to a real-world machine learning project.

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Define the problem statement
Collect and prepare data
Train and evaluate models
Deploy the model

Career center

Learners who complete Serverless Machine Learning con TensorFlow en GCP will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers oversee the process of collecting and transforming data, training models, and deploying them into production. They may work as part of a team of Data Scientists and Data Engineers, and their creations help businesses to make useful predictions.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for extracting insights from an organization's data, and then presenting them in a way that leads to decisions being made. Their work requires knowledge of mathematics and programming, and often they have an advanced degree in a field such as statistics, computer science, or engineering.

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Software Engineer

Machine Learning is a new area of computing that has grown in recent years. This has led to increased demand for engineers who know how to code and program machine learning algorithms. This course would help someone in this role by expanding their knowledge in machine learning. Also, this course uses Google Cloud Platform, which is a commonly used tool for machine learning engineers.

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Data Analyst

Data Analysts make heavy use of machine learning to help businesses of all sizes gain insights from their data. Machine learning can make it possible to identify which customers are at risk of leaving the company, or to personalize each web visitor's experience. This course builds a foundation in machine learning with TensorFlow that can be leveraged to support a career as a Data Analyst.

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Quantitative Analyst

Quantitative Analysts, or Quants as they are more often known, use mathematical and statistical methods to help banks, hedge funds, and other financial institutions to make investment decisions. In recent years there has been a push within the Quant community to incorporate machine learning methods into their investment strategies.

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Research Scientist

Research Scientists perform research with the goal of advancing knowledge in their field. In the field of machine learning, research is being conducted on a wide range of topics, such as deep learning, natural language processing, and computer vision. This course teaches TensorFlow, which is one of the most popular frameworks used for machine learning research today.

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Product Manager

Product Managers are responsible for the overall success of a product, from its inception to its launch and beyond. They work with engineers, designers, and marketers to ensure that the product meets the needs of its users. Product Managers who understand machine learning will be able to make better decisions about which features to build and how to market the product.

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Business Analyst

Business Analysts use their knowledge of business and technology to help companies improve their performance. They may work on a variety of projects, such as developing new products or processes, or improving customer service. Machine learning can be used to automate many of the tasks that Business Analysts perform.

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Consultant

Consultants help companies to solve problems and improve their performance. They may work on a variety of projects, such as developing new strategies, or improving operations. Machine learning can be used to help Consultants to make better recommendations to their clients.

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Technical Writer

Technical Writers create documentation for software and hardware products. They must be able to understand complex technical concepts and explain them clearly to a non-technical audience. Machine learning is a complex topic, so Technical Writers who understand it will be in high demand.

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Teacher

Teachers educate students in a variety of subjects, including math, science, and English. Machine learning is becoming increasingly important in many fields, so Teachers who understand it will be able to better prepare their students for the future.

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Librarian

Librarians help people to find and access information. Machine learning can be used to improve the way that libraries organize and search their collections. This course may be useful for Librarians who want to learn more about machine learning and how it can be used in libraries.

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Customer Service Representative

Customer Service Representatives help customers with their questions and problems. Machine learning can be used to automate many of the tasks that Customer Service Representatives perform. This course may be useful for Customer Service Representatives who want to learn more about machine learning and how it can be used to improve their work.

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Salesperson

Salespeople sell products and services to customers. Machine learning can be used to help Salespeople to identify potential customers and to close deals. This course may be useful for Salespeople who want to learn more about machine learning and how it can be used to improve their sales performance.

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Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. Machine learning can be used to help Marketing Managers to target their campaigns and to measure their effectiveness. This course may be useful for Marketing Managers who want to learn more about machine learning and how it can be used to improve their marketing campaigns.

See salaries and explore the career path for Marketing Manager