For you to more deeply understand these concepts, I'd like to invite you in this class to see, run, and maybe later write code yourself to implement these concepts.

為了讓大家更深入地理解這些概念，我想請大家在這節課上看看、運行一下，也許以後還可以自己編寫代碼來實現這些概念。

The most widely used tool by machine learning and data science practitioners today is the Jupyter Notebook.

目前，機器學習和數據科學從業人員使用最廣泛的工具是 Jupyter Notebook。

This is a default environment that a lot of us use to code up and experiment and try things out.

這是一個默認環境，我們很多人都用它來編寫代碼、進行實驗和嘗試。

So in this class, right here in your web browser, you'll be able to use a Jupyter Notebook environment to test out some of these ideas for yourself as well.

是以，在這堂課上，你也可以在網絡瀏覽器中使用 Jupyter Notebook 環境來測試這些想法。

This is not some made up simplified environment.

這不是什麼虛構的簡化環境。

This is the exact same environment, the exact same tool, the Jupyter Notebook that developers are using in many large companies right now.

這與許多大公司的開發人員正在使用的 Jupyter Notebook 環境、工具完全相同。

One type of lab that you see throughout this class are optional labs, which are ones you can open and run one line at a time, with usually not needing to write any code yourself.

在這門課中，有一種類型的實驗是可選實驗，你可以每次打開並運行一行，通常不需要自己編寫任何代碼。

So optional labs are designed to be very easy, and I can guarantee you will get full marks for every single one of them, because there are no marks.

是以，選修實驗的設計非常簡單，而且我可以保證你的每一個實驗都能得滿分，因為沒有分數。

All you need to do is open it up and just run the code we've provided.

您只需打開它，然後運行我們提供的代碼即可。

By reading through and running the code in the optional labs, you see how machine learning code runs.

通過閱讀和運行可選實驗室中的代碼，您可以瞭解機器學習代碼是如何運行的。

You should be able to complete them relatively quickly, just by running it one line at a time from top to bottom.

只要從上到下一行一行地運行，就能比較快地完成。

Optional labs are completely optional, so you don't have to do them at all if you don't want to.

選修實驗室完全是可選的，所以如果你不想做，完全可以不做。

But I hope you will take a look, because running through them will give you a deeper feel, give you a little bit more experience with what's machine learning algorithms, what machine learning code actually looks like.

但我希望你能看一看，因為瀏覽這些內容會讓你有更深的感受，讓你對機器學習算法、機器學習代碼的實際樣子有更多的體驗。

Starting next week, there'll also be some practice labs, which will give you an opportunity to write some of that code yourself.

從下週開始，我們還將提供一些實踐實驗室，讓您有機會親自編寫一些代碼。

But we'll get to that next week, so don't worry about it for now, and I hope you just go through the next optional lab, and get through the rest of the content for this week.

但我們下週會討論這個問題，所以現在不用擔心，我希望你們只需完成下一個可選實驗，並完成本週剩下的內容。

So let's take a look at an example of a notebook.

讓我們來看一個筆記本的例子。

Here's what you see when you go to the first optional lab.

下面是您進入第一個可選實驗室時看到的情況。

Feel free to scroll up and down, and browse, and mouse over the different menus, and take a look at the different options here.

請隨意上下滾動、瀏覽，並將滑鼠移至不同的菜單上，查看這裡的不同選項。

You might notice that there are two types of these blocks, also called cells in the notebook, and there are two types of cells.

您可能會注意到，這些塊有兩種類型，在筆記本中也稱為單元格，而單元格也有兩種類型。

One is what's called a markdown cell, which means basically a bunch of text.

一個是所謂的 markdown 單元，基本上就是一堆文本。

Here, you can actually edit the text if you don't like the text that we wrote.

在這裡，如果你不喜歡我們寫的文字，實際上可以對文字進行編輯。

But this is text that describes the code.

但這是描述代碼的文字。

Then there's a second type of block or cell, which looks like this, which is a code cell.

還有第二種類型的區塊或單元格，看起來像這樣，這就是代碼單元格。

Here, we've already provided the code, and if you want to run this code cell, hitting Shift Enter will run the code in this code cell.

在這裡，我們已經提供了代碼，如果您想運行這個代碼單元格，按下 Shift Enter 鍵就可以運行這個代碼單元格中的代碼。

By the way, if you click on the markdown cell, so it's showing all this formatting, go ahead and hit Shift Enter on your keyboard as well, and that will also convert it back to this nicely formatted text.

順便說一下，如果你點擊標記符單元格，使其顯示所有這些格式，繼續按鍵盤上的 Shift Enter 鍵，也會將其轉換為格式化的文本。

This optional lab shows some common Python code, so you can go ahead and run this afterwards in your own Jupyter Notebook.

這個可選實驗展示了一些常見的 Python 代碼，所以你可以在自己的 Jupyter Notebook 中運行這些代碼。

When you jump into this notebook yourself, what I'd like you to do is select the cells and hit Shift Enter.

當你跳轉到這個筆記本時，我想讓你做的是選擇單元格並按下 Shift Enter 鍵。

Well, read through the code, see if it makes sense.

那就通讀一下代碼，看看是否有道理。

Try to make a prediction about what you think this code will do, and then hit Shift Enter, and then see what the code actually does.

試著預測一下你認為這段代碼會做什麼，然後按下 Shift Enter 鍵，再看看這段代碼實際做了什麼。

If you feel like it, feel free to go in and edit the code.

如果你願意，可以編輯代碼。

Change the code and then run it and see what happens.

修改代碼後運行，看看會發生什麼。

If you haven't played in a Jupyter Notebook environment before, I hope you become more familiar with Python in a Jupyter Notebook.

如果你以前沒有在 Jupyter Notebook 環境中玩過，我希望你能更熟悉 Jupyter Notebook 中的 Python。

I spend a lot of hours playing around in Jupyter Notebooks, and so I hope you have fun with them too.

我花了很多時間玩 Jupyter 筆記本，希望你也能從中獲得樂趣。

After that, I look forward to seeing you in the next video, where we'll take the supervised learning problem and start to flesh out our first supervised learning algorithm.

之後，我期待在下一個視頻中與大家見面，我們將討論監督學習問題，並開始充實我們的第一個監督學習算法。

I hope that'll be fun too and look forward to seeing you there.

我希望那也會很有趣，並期待在那裡見到你們。