You 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.

你將學習到最重要的機器學習算法，其中一些正是當今大型人工智能或大型科技公司正在使用的算法，你還將瞭解到人工智能領域的最先進技術。

Beyond learning the algorithms though, in this class, you also learn all the important practical tips and tricks for making them perform well, and you get to implement them and see how they work for yourself.

除了學習算法之外，在這門課上，你還能學到所有重要的實用技巧和竅門，讓它們發揮出色的性能，你還能親身實踐，看看它們是如何工作的。

Why is machine learning so widely used today?

機器學習如今為何得到如此廣泛的應用？

Machine learning had grown up as a subfield of AI or artificial intelligence.

機器學習是人工智能的一個子領域。

We wanted to build intelligent machines, and it turns out that there are 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.

我們想製造智能機器，而事實證明，我們可以通過編程讓機器做一些基本的事情，比如如何像 GPS 那樣找到從 A 到 B 的最短路徑。

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.

但在大多數情況下，我們只是不知道如何編寫一個明確的程序來完成許多更有趣的事情，比如進行網絡搜索、識別人類語言、通過 X 射線診斷疾病或製造一輛自動駕駛汽車。

The only way we knew how to do these things was to have a machine learn to do it by itself.

我們知道如何做這些事情的唯一方法就是讓機器學會自己做。

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.

就我而言，當我創建並上司谷歌大腦團隊時，我研究的問題包括語音識別、谷歌地圖的計算機視覺、街景影像和廣告。

Or leading AI at Baidu, I worked on everything from AI for augmented reality to combating payment fraud to leading a self-driving car team.

在百度上司人工智能工作期間，我從事了從增強現實人工智能到打擊支付欺詐再到上司自動駕駛汽車團隊的各種工作。

Most recently, at Landing AI, AI Fund at Stanford University, I've been getting to work on AI applications in manufacturing, large-scale agriculture, healthcare, e-commerce, and other problems.

最近，我在斯坦福大學的人工智能基金 Landing AI 工作，研究人工智能在製造業、大規模農業、醫療保健、電子商務等領域的應用。

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.

如今，有成千上萬，甚至上百萬人在從事機器學習應用工作，他們可以向你講述自己在機器學習方面的類似故事。

When you've learned these skills, I hope that you too will find it great fun to dabble in exciting different applications and maybe even different industries.

當你學會了這些技能後，我希望你也會發現，涉足令人興奮的不同應用甚至不同行業是一件非常有趣的事情。

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.

事實上，我很難想象現在或不久的將來，機器學習會對哪個行業產生重大影響。

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.

展望未來，包括我在內的許多人都對人工智能夢想感到興奮，希望有一天能製造出像你我一樣智能的機器。

This is sometimes called Artificial General Intelligence or AGI.

這有時被稱為人工通用智能或 AGI。

I think AGI has been overhyped and we're still a long way away from that goal.

我認為 AGI 被誇大了，我們離這個目標還有很長的路要走。

I don't know if it'll take 50 years or 500 years or longer to get there, but most AI researchers believe that the best way to get closer to what that goal is by using learning algorithms, maybe ones that take some inspiration from how the human brain works.

我不知道實現這一目標需要 50 年、500 年還是更長的時間，但大多數人工智能研究人員認為，接近這一目標的最佳途徑是使用學習算法，也許是從人腦工作原理中汲取靈感的算法。

You'll also hear a little more about this quest for AGI later in this course.

在本課程的稍後部分，您還會聽到更多關於探索 AGI 的內容。

According to a study by McKinsey, AI and machine learning is estimated to create an additional US$13 trillion of value annually by the year 2030.

根據麥肯錫的一項研究，到 2030 年，人工智能和機器學習預計每年將創造 13 萬億美元的額外價值。

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 is yet to be created outside the software industry in sectors such as retail, travel, transportation, automotive, materials manufacturing, and so on.

儘管機器學習已經在軟件行業創造了巨大的價值，但我認為，在軟件行業之外，零售、旅遊、運輸、汽車、材料製造等行業可能還有更大的價值有待創造。

Because of the massive untapped opportunities across so many different sectors, today there is a vast unfulfilled demand for this skill set.

由於許多不同行業都存在大量尚未開發的機會，如今對這一技能組合的需求仍未得到滿足。

That's why this is such a great time to be learning about machine learning.

這就是為什麼現在是學習機器學習的大好時機。

If you find machine learning applications exciting, I hope you stick with me through this course.

如果你覺得機器學習應用令人興奮，我希望你能跟我一起完成這門課程。

I can almost guarantee that you'll find mastering these skills worthwhile.

我幾乎可以保證，你會發現掌握這些技能是值得的。

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.

在下一個視頻中，我們將對什麼是機器學習進行更正式的定義，並開始討論機器學習問題和算法的主要類型。

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.

你將掌握一些主要的機器學習術語，並開始瞭解不同的算法以及每種算法何時適用。

Let's go on to the next video.

讓我們繼續觀看下一段視頻。