目录
Applications of machine learning
Welcome to Machine learning. What is machine learning?
You probably use it many times a day without even knowing it.
Anytime you want to find out something like how do I make a sushi roll?
You can do a web search on Google, Bing or Baidu to find out.
And that works so well because their machine learning software has figured out
how to rank web pages.
Or when you upload pictures to Instagram or Snapchat and
think to yourself, I want to tag my friends so they can see their pictures.
Well these apps can recognize your friends in your pictures and label them as well.
That's also machine learning.
Or if you've just finished watching a Star Wars movie on the video
streaming service and you think what other similar movies can I watch?
Well the streaming service will likely use machine learning to recommend something
that you might like.
Each time you use voice to text on your phone to write a text message.
Hey Siri play a song by Rihanna,
or ask your other phone okay Google show me Indian restaurants near me.
That's also machine learning.
Each time you receive an email titled, Congratulations!
You've won a million dollars.
Well maybe you're rich, congratulations.
Or more likely your email service will probably flag it as spam.
That too is an application of machine learning.
Beyond consumer applications that you might use, AI is also rapidly
making its way into big companies and into industrial applications.
For example, I'm deeply concerned about climate change, and I'm glad to see
that machine learning is already hoping to optimize wind turbine power generation.
Or in healthcare,
is starting to make its way into hospitals to help doctors make accurate diagnosis.
Or recently at Landing AI have been doing a lot of work,
putting computer vision into factories to help inspect if something coming
off the assembly line has any defects.
That's machine learning,
it's the science of getting computers to learn without being explicitly programmed.
In this course, you learn about machine learning and
get to implement machine learning and code yourself.
Millions of others have taken the earlier version of this course,
which is of course, that led to the founding of Coursera.
And many learners ended up building exciting machine learning systems or
even pursuing very successful careers in AI.
I'm excited that you're on this journey with me.
Welcome and let's get started.
欢迎来到机器学习。什么是机器学习？你可能一天用很多次，甚至不知道。任何时候你想知道我怎么做寿司卷？你可以在谷歌、必应或百度上搜索一下。这很好，因为他们的机器学习软件已经解决了如何对网页进行排名。或者当你把照片上传到Instagram或Snapchat，然后自言自语时，我想给我的朋友贴上标签，让他们可以看到他们的照片。这些应用程序可以识别照片中的朋友，并为他们添加标签。这也是机器学习。或者，如果你刚刚在视频流媒体服务上看完《星球大战》电影，你想我还能看什么类似的电影？流媒体服务可能会使用机器学习来推荐你可能喜欢的东西。每次使用语音在手机上发送文字信息时，都会发送文字信息。>>嘿，安德鲁，最近怎么样？>>或者告诉你的电话。嘿，Siri播放蕾哈娜的歌，或者问你的另一个电话好吗，谷歌给我看看我附近的印度餐馆。
这也是机器学习。每次你收到一封标题为“恭喜你！你赢了一百万美元。也许你有钱了，恭喜你。或者更可能的是，您的电子邮件服务可能会将其标记为垃圾邮件。这也是机器学习的一个应用。除了你可能使用的消费应用程序，人工智能也正在迅速进入大公司和工业应用程序。例如，我非常关注气候变化，我很高兴看到机器学习已经希望优化风力发电。或者在医疗保健领域，正开始进入医院，帮助医生做出准确的诊断。或者最近在Landing，AI做了很多工作，将计算机视觉应用到工厂，以帮助检查从装配线上出来的东西是否有任何缺陷。这就是机器学习，这是一门让计算机在没有明确编程的情况下学习的科学。在本课程中，您将学习机器学习，并自己实现机器学习和编码。数以百万计的其他人参加了这门课程的早期版本，这当然是导致Coursera成立的原因。许多学习者最终建立了令人兴奋的机器学习系统，甚至在人工智能领域追求非常成功的职业。我很高兴你能和我一起踏上这段旅程。欢迎大家，让我们开始吧。
In this class, you'll learn about the state of the art and also practice implementing machine learning algorithms yourself. You'll learn about the most important machine learning algorithms, some of which are exactly what's being used in large AI or large tech companies today and you get a sense of what is the state of the art in AI. Beyond learning the algorithms though, in this class, you'll also learn all the important practical tips and tricks for making them perform well. You get to implement them and see how they work for yourself. Why is machine learning so widely used today? Machine Learning had grown up as a sub-field of AI or artificial intelligence. We wanted to build intelligent machines. It turns out that there are a few basic things that we could program a machine to do, such as how to find the shortest path from a to b, like in your GPS. But for the most part, we just did not know how to write an explicit program to do many of the more interesting things, such as perform web search, recognize human speech, diagnose diseases from X-rays or build a self-driving car. The only way we knew how to do these things was to have a machine learn to do it by itself. For me, when I founded and was leading the Google Brain Team, I worked on problems like speech recognition, computer vision for Google Maps, Street View images and advertising, or leading AI Baidu, I worked on everything from AI for augmented reality to combating payment fraud to leading a self-driving car team. Most recently, at landing.AI, AI Fund and Stanford University, I'm beginning to work on AI applications in the factory, large-scale agriculture, health care, e-commerce, and other problems. Today, there are hundreds of thousands, perhaps millions of people working on machine learning applications who could tell you similar stories about their work with machine learning. When you've learned these skills, I hope that you too will find the great fun to dabble in exciting different applications and maybe even different industries. In fact, I find it hard to think of any industry that machine learning is unlikely to touch in a significant way now or in the near future. Looking even further into the future, many people, including me, are excited about the AI dream of someday building machines as intelligent as you or me. This is sometimes called Artificial General Intelligence or AGI. I think AGI has been overhyped and we're still a long way away from that goal. I don't know. It'll take 50 years or 500 years or longer to get there. But mostly AI researchers believe that the best way to get closer toward that goal is by using learning algorithms. Maybe ones that take some inspiration from how the human brain works. You also hear a little more about this Quest for AGI later in this course. According to a study by McKinsey, AI and machine learning is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value that has yet to be created outside the software industry in sectors such as retail, travel, transportation, automotive, materials manufacturing, and so on. Because of the massive untapped opportunities across so many different sectors, today there is a vast unfulfilled demand for this skill set. That's why this is such a great time to be learning about machine learning. If you find machine learning applications exciting, I hope you stick with me through this class. I can almost guarantee that you'll find mastering these skills worthwhile. In the next video, we'll look at a more formal definition of what is machine learning. And we'll begin to talk about the main types of machine learning problems and algorithms. You pick up some of the main machine learning terminology and start to get a sense of what are the different algorithms and when each one might be appropriate. So let's go on to the next video.
在本课程中，您将了解最新技术，并练习自己实现机器学习算法。您将了解最重要的机器学习算法，其中一些算法正是当今大型人工智能或大型科技公司使用的算法，您将了解人工智能的最新技术。不过，除了学习算法之外，在这堂课上，你还将学习所有重要的实用技巧和技巧，让它们表现出色。你可以实施它们，看看它们是如何为你自己工作的。为什么机器学习在今天被如此广泛地使用？机器学习已经成长为人工智能或人工智能的一个子领域。我们想制造智能机器。事实证明，我们可以对机器进行一些基本的编程，例如如何找到从a到b的最短路径，比如在GPS中。但在大多数情况下，我们只是不知道如何编写一个明确的程序来完成许多更有趣的事情，比如执行网络搜索、识别人类语言、从X光诊断疾病或建造一辆自动驾驶汽车。我们知道如何做这些事情的唯一方法是让机器学会自己做。对我来说，当我成立并领导谷歌大脑团队时，我致力于语音识别、谷歌地图的计算机视觉、街景图像和广告等问题，或者领导人工智能百度，我致力了从增强现实的人工智能到打击支付欺诈，再到领导自动驾驶汽车团队的一切。最近，在着陆时。人工智能、人工智能基金和斯坦福大学，我开始研究人工智能在工厂、大规模农业、医疗保健、电子商务和其他问题中的应用。今天，有几十万甚至几百万人在机器学习应用程序上工作，他们可以告诉你他们在机器学习方面的工作经历。当你学会了这些技能后，我希望你也会发现涉足令人兴奋的不同应用程序，甚至不同行业的乐趣。事实上，我发现很难想象机器学习现在或在不久的将来不太可能对任何行业产生重大影响。展望未来，包括我在内的许多人都对人工智能梦想感到兴奋，梦想有一天能建造像你我一样智能的机器。这有时被称为人工智能或AGI。我认为AGI被夸大了，我们离这个目标还有很长的路要走。我不知道。这需要50年或500年或更长的时间才能到达那里。但大多数人工智能研究人员认为，实现这一目标的最佳方法是使用学习算法。也许是从人类大脑的工作方式中获得一些灵感。在本课程稍后的部分中，您还会听到更多关于AGI的探索。根据麦肯锡（McKinsey）的一项研究，到2030年，人工智能和机器学习每年将创造额外的13万亿美元的价值。尽管机器学习已经在软件行业创造了巨大的价值，但我认为，在软件行业之外，在零售、旅游、娱乐等行业，可能还有更大的价值有待创造，交通运输、汽车、材料制造等。由于许多不同行业存在大量未开发的机会，今天对这一技能的需求量巨大，无法满足。这就是为什么现在是学习机器学习的好时机。如果你觉得机器学习应用程序令人兴奋，我希望你在这节课上和我一起学习。我几乎可以保证，你会发现掌握这些技能是值得的。在下一个视频中，我们将看到什么是机器学习的更正式的定义。我们将开始讨论机器学习问题和算法的主要类型。你学习了一些主要的机器学习术语，并开始了解不同的算法是什么，以及每种算法何时合适。所以让我们继续下一个视频。