Acerca de este Curso

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Resultados profesionales del estudiante

43%

comenzó una nueva carrera después de completar estos cursos

44%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

29%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 6 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Alemán (German), Inglés (English), Español (Spanish), Japonés...

Habilidades que obtendrás

TensorflowBigqueryMachine LearningData Cleansing

Resultados profesionales del estudiante

43%

comenzó una nueva carrera después de completar estos cursos

44%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

29%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 6 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Alemán (German), Inglés (English), Español (Spanish), Japonés...

Instructor

ofrecido por

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Google Cloud

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up92%(2,809 calificaciones)Info
Semana
1

Semana 1

9 minutos para completar

Introduction

9 minutos para completar
2 videos (Total 9 minutos)
2 videos
Intro to Qwiklabs5m
1 hora para completar

Practical ML

1 hora para completar
10 videos (Total 62 minutos)
10 videos
Supervised Learning5m
Regression and Classification11m
Short History of ML: Linear Regression7m
Short History of ML: Perceptron5m
Short History of ML: Neural Networks7m
Short History of ML: Decision Trees5m
Short History of ML: Kernel Methods4m
Short History of ML: Random Forests4m
Short History of ML: Modern Neural Networks8m
1 ejercicio de práctica
Module Quiz6m
1 hora para completar

Optimization

1 hora para completar
13 videos (Total 60 minutos)
13 videos
Defining ML Models4m
Introducing the Natality Dataset6m
Introducing Loss Functions6m
Gradient Descent5m
Troubleshooting a Loss Curve2m
ML Model Pitfalls6m
Lab: Introducing the TensorFlow Playground6m
Lab: TensorFlow Playground - Advanced3m
Lab: Practicing with Neural Networks6m
Loss Curve Troubleshooting1m
Performance Metrics3m
Confusion Matrix5m
1 ejercicio de práctica
Module Quiz6m
3 horas para completar

Generalization and Sampling

3 horas para completar
9 videos (Total 64 minutos)
9 videos
Generalization and ML Models6m
When to Stop Model Training5m
Creating Repeatable Samples in BigQuery6m
Demo: Splitting Datasets in BigQuery8m
Lab Introduction1m
Lab Solution Walkthrough9m
Lab Introduction2m
Lab Solution Walkthrough23m
1 ejercicio de práctica
Module Quiz12m
3 minutos para completar

Summary

3 minutos para completar
1 video (Total 3 minutos)
1 video

Revisiones

Principales revisiones sobre LAUNCHING INTO MACHINE LEARNING

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Acerca de Programa especializado: Machine Learning with TensorFlow on Google Cloud Platform

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <...
Machine Learning with TensorFlow on Google Cloud Platform

Preguntas Frecuentes

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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