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.
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Google 云端平台课程信息
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中级
完成时间大约为22 小时
英语（English）
字幕：英语（English）
您将获得的技能
TensorflowBigqueryMachine LearningData Cleansing
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
中级
完成时间大约为22 小时
英语（English）
字幕：英语（English）
提供方
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.
授课大纲 - 您将从这门课程中学到什么
完成时间为 8 分钟
Introduction to Course
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.
完成时间为 8 分钟
2 个视频 （总计 8 分钟）
完成时间为 4 小时
Improve Data Quality and Exploratory Data Analysis
In this module, we will introduce data quality issues and how to improve them. We will then look at exploratory data anlaysis.
完成时间为 4 小时
5 个视频 （总计 29 分钟）, 1 个阅读材料, 4 个测验
完成时间为 8 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.
完成时间为 8 小时
13 个视频 （总计 63 分钟）, 1 个阅读材料, 11 个测验
完成时间为 3 小时
Optimization
In this module we will walk you through how to optimize your ML models.
完成时间为 3 小时
13 个视频 （总计 61 分钟）, 1 个阅读材料, 4 个测验
完成时间为 4 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. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.
完成时间为 4 小时
9 个视频 （总计 64 分钟）, 1 个阅读材料, 4 个测验
完成时间为 3 小时
Summary
完成时间为 3 小时
1 个视频 （总计 4 分钟）, 3 个阅读材料, 1 个测验
审阅
来自LAUNCHING INTO MACHINE LEARNING的热门评论
由 OD 提供May 30, 2020
Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.
由 PT 提供Dec 1, 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 :)
由 PA 提供Aug 3, 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.
由 CR 提供Aug 12, 2021
Interesting course, and the technical details during week 3 and 4 were highly appreicated. The labs could have been a bit better put together, but all round happy with this course!
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