Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.
Course Objectives:
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
Этот курс входит в специализацию ''Специализация Machine Learning with TensorFlow on Google Cloud Platform'
Launching into Machine Learning
от партнера
Программа курса: что вы изучите
Неделя
1Introduction
In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.
2 видео ((всего 9 мин.))
Practical ML
In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.
10 видео ((всего 62 мин.)), 1 тест
10 видео
Introduction1мин
Supervised Learning5мин
Regression and Classification11мин
Short History of ML: Linear Regression7мин
Short History of ML: Perceptron5мин
Short History of ML: Neural Networks7мин
Short History of ML: Decision Trees5мин
Short History of ML: Kernel Methods4мин
Short History of ML: Random Forests4мин
Short History of ML: Modern Neural Networks8мин
1 практическое упражнение
Module Quiz6мин
Optimization
In this module we will walk you through how to optimize your ML models.
13 видео ((всего 61 мин.)), 1 тест
13 видео
Defining ML Models4мин
Introducing the Natality Dataset6мин
Introducing Loss Functions6мин
Gradient Descent5мин
Troubleshooting a Loss Curve2мин
ML Model Pitfalls6мин
Lab: Introducing the TensorFlow Playground6мин
Lab: TensorFlow Playground - Advanced3мин
Lab: Practicing with Neural Networks6мин
Loss Curve Troubleshooting1мин
Performance Metrics3мин
Confusion Matrix5мин
1 практическое упражнение
Module Quiz6мин
Generalization and Sampling
Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world.
9 видео ((всего 64 мин.)), 3 тестов
9 видео
Introduction1мин
Generalization and ML Models6мин
When to Stop Model Training5мин
Creating Repeatable Samples in BigQuery6мин
Demo: Splitting Datasets in BigQuery8мин
Lab Introduction1мин
Lab Solution Walkthrough9мин
Lab Introduction2мин
Lab Solution Walkthrough23мин
1 практическое упражнение
Module Quiz12мин
52%
начал новую карьеру, пройдя эти курсы
42%
получил значимые преимущества в карьере благодаря этому курсу
16%
стал больше зарабатывать или получил повышение
Лучшие рецензии
автор: PT•Dec 2nd 2018
This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)
автор: PA•Aug 4th 2018
Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.
О Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
О специализации ''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 <
Часто задаваемые вопросы
Можно ли ознакомиться с курсом до регистрации?
Да, до регистрации можно просмотреть первое видео и программу курса. Чтобы получить полный доступ к материалам курса, необходимо оплатить его.
Когда я получу доступ к лекциям и заданиям?
Регистрируясь на курс до начала сессии, вы получаете доступ ко всем видеолекциям и материалам для самостоятельного изучения. Задания можно отправлять после начала сессии.
Что я получу, зарегистрировавшись на курс?
После регистрации на курс и начала сессии вы получаете доступ ко всем видео и другим ресурсам курса, включая материалы для самостоятельного изучения и форумы обсуждений. Вы сможете выполнять тренировочные, а также сдавать обязательные оцениваемые задания и получить сертификат о прохождении курса.
Когда я получу сертификат о прохождении курса?
Если вы успешного пройдете курс, на странице ваших достижений появится электронный сертификат, который можно распечатать или прикрепить к профилю LinkedIn.
Почему я не могу прослушать этот курс?
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.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
Остались вопросы? Посетите Центр поддержки учащихся.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2019 Все права защищены.
