***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***
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
SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Machine Learning with TensorFlow on Google Cloud Platform’ specialization.
Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).
You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.
Your challenge awaits – begin learning on Coursera today!
本课程是 Machine Learning with TensorFlow on Google Cloud Platform 专项课程 专项课程的一部分
Launching into Machine Learning
提供方
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 9 分钟
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.
2 个视频 （总计 9 分钟）
完成时间为 1 小时
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 个视频 （总计 62 分钟）, 1 个测验
10 个视频
Introduction1分钟
Supervised Learning5分钟
Regression and Classification11分钟
Short History of ML: Linear Regression7分钟
Short History of ML: Perceptron5分钟
Short History of ML: Neural Networks7分钟
Short History of ML: Decision Trees5分钟
Short History of ML: Kernel Methods4分钟
Short History of ML: Random Forests4分钟
Short History of ML: Modern Neural Networks8分钟
1 个练习
Module Quiz6分钟
完成时间为 1 小时
Optimization
In this module we will walk you through how to optimize your ML models.
13 个视频 （总计 61 分钟）, 1 个测验
13 个视频
Defining ML Models4分钟
Introducing the Natality Dataset6分钟
Introducing Loss Functions6分钟
Gradient Descent5分钟
Troubleshooting a Loss Curve2分钟
ML Model Pitfalls6分钟
Lab: Introducing the TensorFlow Playground6分钟
Lab: TensorFlow Playground - Advanced3分钟
Lab: Practicing with Neural Networks6分钟
Loss Curve Troubleshooting1分钟
Performance Metrics3分钟
Confusion Matrix5分钟
1 个练习
Module Quiz6分钟
完成时间为 3 小时
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 个视频 （总计 64 分钟）, 3 个测验
9 个视频
Introduction1分钟
Generalization and ML Models6分钟
When to Stop Model Training5分钟
Creating Repeatable Samples in BigQuery6分钟
Demo: Splitting Datasets in BigQuery8分钟
Lab Introduction1分钟
Lab Solution Walkthrough9分钟
Lab Introduction2分钟
Lab Solution Walkthrough23分钟
1 个练习
Module Quiz12分钟
4.5
143 个审阅51%
完成这些课程后已开始新的职业生涯
45%
通过此课程获得实实在在的工作福利
21%
加薪或升职
热门审阅
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 :)
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.
关于 Google 云端平台
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.
关于 Machine Learning with TensorFlow on Google Cloud Platform 专项课程
>>>Look for details below for COMPLETION CHALLENGE, receive up to $150 in Qwiklabs credits<<<
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.
COMPLETION CHALLENGE
For every course you complete before May 5, 2019, we will send you 30 Qwiklabs credits (upto $150 USD value)!
> 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 <
常见问题
我能否在注册前预览课程？
是的，您可以在注册之前预览第一个视频和查看授课大纲。您必须购买课程，才能访问预览不包括的内容。
我什么时候能够访问课程视频和作业？
如果您决定在班次开始日期之前注册课程，那么您将可以访问课程的所有课程视频和阅读材料。班次开始之后，您便可以提交作业。
我注册之后会得到什么？
在您注册且班次开课之后，您将可以访问所有视频和其他资源，包括阅读材料内容和课程论坛。您将能够查看和提交练习作业，并完成所需的评分作业以获得成绩和课程证书。
我什么时候会收到课程证书？
如果您成功完成课程，您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。
我为什么不能旁听此课程？
该课程是 Coursera 上提供的为数不多的课程之一，目前只对已购买课程或已获得助学金的学生开放。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
