Об этом курсе: Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of 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
Для кого этот курс: This course is aimed at data scientists, data engineers and programmers interested in learning how to apply machine learning in practice.
Автор: Google Cloud
| Основные сведения | Course 2 of 5 in the Machine Learning with TensorFlow on Google Cloud Platform Specialization |
| Уровень | Intermediate |
| Язык | English |
| Требования к аппаратным средствам | You'll need to use a desktop web browser to access labs. |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
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Программа курса
НЕДЕЛЯ 1
Introduction
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.
1 видео
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
- Видео: Supervised Learning
- Видео: Regression and Classification
- Видео: Short History of ML: Linear Regression
- Видео: Short History of ML: Perceptron
- Видео: Short History of ML: Neural Networks
- Видео: Short History of ML: Decision Trees
- Видео: Short History of ML: Kernel Methods
- Видео: Short History of ML: Random Forests
- Видео: Short History of ML: Modern Neural Networks
- Discussion Prompt: Modern Neural Networks
Оцениваемый: Module Quiz
Optimization
In this module we will walk you through how to optimize your ML models.
13 видео
- Видео: Defining ML Models
- Видео: Introducing the Natality Dataset
- Видео: Introducing Loss Functions
- Видео: Gradient Descent
- Видео: Troubleshooting a Loss Curve
- Видео: ML Model Pitfalls
- Видео: Lab: Introducing the TensorFlow Playground
- Видео: Lab: TensorFlow Playground - Advanced
- Видео: Lab: Practicing with Neural Networks
- Видео: Loss Curve Troubleshooting
- Видео: Performance Metrics
- Видео: Confusion Matrix
Оцениваемый: Module Quiz
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
- Видео: Generalization and ML Models
- Видео: When to Stop Model Training
- Видео: Creating Repeatable Samples in BigQuery
- Видео: Demo: Splitting Datasets in BigQuery
- Видео: Lab Introduction
- Видео: Lab Solution Walkthrough
- Видео: Lab Introduction
- Видео: Lab Solution Walkthrough
Оцениваемый: Lab: Creating Repeatable Dataset Splits in BigQuery
Оцениваемый: Lab: Exploring and Creating ML Datasets
Оцениваемый: Module Quiz
Summary
1 видео
Часто задаваемые вопросы
Как это работает
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Авторы
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Рейтинги и отзывы
An excellent course, thank you Googlers. My favorite part is the intuition about ML.
Very useful intro to data processing, specially the hashing mechanism to partition the datasets.
The last lab was confusing because the data might have some invalid value. in the jupyter notebook, the sin, and arcsin values were not getting computed (probably?) as I got warning from python .
Nice introduction course!
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AA
Poor lab setup - have to repeat the step of creating a vm for every lab.