New Crowdin translations

af-ZA/meta.yml

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+---
+title: Smart classroom assistant
+hero_image: images/banner.png
+description: Create a virtual classroom assistant that reacts to commands
+version: 4
+listed: true
+copyedit: true
+last_tested: "2019-05-09"
+steps:
+  - 
+    title: Introduction
+  - 
+    title: How to create a project
+  - 
+    title: Add a list of rules
+  - 
+    title: Collect examples for training
+  - 
+    title: Train and test a machine learning model
+  - 
+    title: Use the machine learning model in Scratch
+  - 
+    title: How to use confidence scores
+  - 
+    title: "Challenge: more items to control"
+    challenge: true
+  - 
+    title: What next?

af-ZA/step_1.md

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+## Introduction
+
+In this project you will use [machinelearningforkids.co.uk](https://machinelearningforkids.co.uk){:target="_blank"} to make a smart virtual classroom assistant that reacts to what you say to it. You’ll be able to control the virtual devices in the classroom by typing in commands!
+
+First, you’ll create an assistant that uses a list of rules for understanding commands, and you'll learn why that approach isn’t very good.
+
+Next, you will teach the assistant to recognise commands for different devices by training it using examples of each command.
+
+### What you will make
+
+\--- print-only \---
+
+![Complete project](images/what-you-will-make.png)
+
+\--- /print-only \---
+
+\--- no-print \---
+
+![Complete project GIF](images/smart-classroom.gif)
+
+\--- /no-print \---
+
+\--- collapse \---
+---
+title: What you will learn
+---
+
++ How to train and test a machine learning model
++ Why this approach is better than using a long list of rules
++ How confidence scores can improve the user experience
+
+\--- /collapse \---
+
+\--- collapse \---
+---
+title: What you will need
+---
+
++ A computer connected to the internet
+
+\--- /collapse \---
+
+\--- collapse \---
+---
+title: Additional information for educators
+---
+
+If you need to print this project, please use the [printer-friendly version](https://projects.raspberrypi.org/en/projects/smart-classroom/print){:target="_blank"}.
+
+\--- /collapse \---
+
+### Licence
+
+This project is dual-licensed under both a [Creative Commons Attribution Non-Commercial Share-Alike License](http://creativecommons.org/licenses/by-nc-sa/4.0/){:target="_blank"} and an [Apache License Version 2.0](http://www.apache.org/licenses/LICENSE-2.0){:target="_blank"}.
+
+We'd like to thank Dale from machinelearningforkids.co.uk for all his work on this project.

af-ZA/step_2.md

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+## How to create a project
+
+\--- task \---
++ Go to [machinelearningforkids.co.uk](https://machinelearningforkids.co.uk/){:target="_blank"} in a web browser.
+
++ Click on **Get Started**
+
++ Click on **Try it now**. \--- /task \---
+
+\--- task \---
++ Click on **Projects** in the menu bar at the top.
+
++ Click on the **+ Add a new project** button.
+
++ Name your project 'smart classroom' and set it to learn to recognise **text**.  Then click on **Create**. ![Creating a project](images/create-project-annotated.png)
+
++ You should now see 'smart classroom' in the projects list. Click on this project. ![Project list with smart classroom listed](images/projects-list-annotated.png) \--- /task \---
+
+\--- task \---
+
+Now get a project ready in Scratch.
+
++ Click on **Make**. ![Project main menu](images/project-make-annotated.png)
+
++ Click on **Scratch 3**.
+
++ Click on **Scratch by itself**. The page then warns you that you haven’t done any machine learning yet. Click on **Scratch by itself** to launch Scratch.
+
++ Click on **Project templates**. ![Scratch menu bar](images/project-templates-annotated.png)
+
++ Click on the **Smart Classroom** template.
+
+![Scratch template project](images/scratch-template.png) \--- /task \---

af-ZA/step_3.md

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+## Add a list of rules
+
+In this step, you will edit the project to include a list of rules to activate and de-activate the fan and the lamp.
+
+\--- task \---
++ Click the **classroom** sprite to select it, as shown below:
+
+![Scratch template project](images/scratch-template-annotated.png)
+
++ Click on the **Scripts** tab and create the following script:
+
+```blocks3
+when green flag clicked
+forever
+ask [Enter your command] and wait
+if <(answer) = [Turn on the fan]> then
+broadcast (turn-fan-on v)
+end
+if <(answer) = [Turn off the fan]> then
+broadcast (turn-fan-off v)
+end
+if <(answer) = [Turn on the lamp]> then
+broadcast (turn-lamp-on v)
+end
+if <(answer) = [Turn off the lamp]> then
+broadcast (turn-lamp-off v)
+end
+end
+```
+
++ Click on **File** and then on **Save to your computer**, and save the program to a file. \--- /task \---
+
+\--- task \---
+
++ Click on the **green flag** to test your program.
+
+![Scratch interface just after green flag is clicked](images/click-flag-annotated.png)
+
++ Type in a command and watch the program react! The following commands should all work:
+    * “Turn on the lamp”
+    * “Turn off the lamp”
+    * “Turn on the fan”
+    * “Turn off the fan”
+
+* Type in anything else, and your program does nothing! Even if you make a small spelling mistake, the program does not react.
+
+\--- /task \---
+
+You’re telling your virtual classroom assistant to react to commands using a simple rules-based approach. But if you wanted your program to understand commands that are phrased differently, you would need to add extra `if` blocks.
+
+The problem with this rules-based approach is that you need to exactly predict all the commands the smart classroom assistant will get. Listing every possible command would take a very, very long time.
+
+Next, you will try a better approach: teaching the computer to recognise commands by itself.

af-ZA/step_4.md

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+## Collect examples for training
+
+\--- task \---
++ Close the Scratch window and go back to the Training tool.
+
++ Click on the **< Back to project** link. \--- /task \---
+
+\--- task \---
++ Click on the **Train** button. ![Project main menu](images/project-train-annotated.png)
+
+You need to collect some examples to train the computer. To collect different examples, you need to create 'buckets' to put the examples in.
+
++ To create a bucket, click on **+ Add new label** and call the bucket “fan on”. Click on **+ Add new label** again and create a second bucket called “fan off”. Create a third and a fourth bucket called “lamp on” and "lamp off". ![4 empty classes named fan_on, fan_off, lamp_on and lamp_off](images/empty-buckets.png)
+
++ Click on the **Add example** button in the “fan on” bucket, and type in a command asking for the fan to be turned on. For example, you could type “Please can you switch on the fan”.
+
++ Click on the **Add example** button in the “fan off” bucket, and type in a command asking for the fan to be switched off. For example, you could type “I want the fan off now”.
+
++ Do the same for the “lamp on” and “lamp off” buckets.
+
+\--- /task \---
+
+\--- task \---
++ Continue to **Add example**s until you have at least **six** examples in **each** bucket.
+
+Be imaginative! Try and think of lots of different ways to ask each command. For example:
+
++ For “fan on”, you could complain that you’re too hot.
++ For “fan off”, you could complain that it’s too breezy.
++ For “lamp on”, you could complain that you can’t see.
++ For “lamp off”, you could complain that it’s too bright.
+
+![4 empty classes named fan_on, fan_off, lamp_on and lamp_off](images/full-buckets.png)
+
+\--- collapse \---
+---
+title: Tips for selecting good examples
+---
++ **More is good**: the more examples you give your program, the better the program should get at recognising your commands.
+
++ **Equal numbers**: add roughly the same number of examples for each command. If you have a lot of examples for one command and not the others, this can affect the way that the program learns to recognise commands.
+
++ **Make the examples really different from each other**: try to come up with lots of different types of examples. For example, make sure that you include some long examples and some very short ones.
+
+\--- /collapse \--- \--- /task \---
+
+In the next step you will train your program to recognise any new command automatically by comparing it to the examples in the four buckets.

af-ZA/step_5.md

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+## Train and test a machine learning model
+
+You will now train the program using the examples, and then test it.
+
+The program will learn from patterns in the examples you give it, such as the choice of words and the way sentences are structured. Then, based on the patterns the program finds, it can interpret new commands.
+
+\--- task \---
++ Click on the **< Back to project** link, then click on **Learn & Test**.
+
++ Click on the **Train new machine learning model** button. If you have enough examples, the program should start to learn how to recognise commands from these examples.
+
+![Annotation pointing to train new machine learning model button](images/train-new-model-annotated.png) \--- /task \---
+
+Wait for the training to complete. This might take a minute or two. While you wait, complete the machine-learning multi-choice quiz at the bottom of the page.
+
+\--- task \--- Once the training has completed, a test box appears. Test your machine learning model to see what it has learned.
+
++ Type in one of the commands you added to a bucket, and then press <kbd>Enter</kbd>. The command should be recognised.
+
++ Type in **commands that are not in the buckets**.
+
+If you’re not happy with how the computer recognises the commands, go back to the previous step and add some more examples. Then **train new machine learning model** again.
+
+![Annotation pointing to train new machine learning model button](images/test-new-model-annotated.png) \--- /task \---
+
+Instead of writing rules for the program, you are giving the program examples. The program uses the examples to train a machine learning **model**.
+
+Because you are supervising the program's training by giving examples, this machine learning approach is called **supervised learning**.

af-ZA/step_6.md

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+## Use the machine learning model in Scratch
+
+Now update your Scratch program to include your machine learning model instead of a rules-based approach.
+
+\--- task \---
++ Click on the **< Back to project** link.
+
++ Click on **Make**.
+
++ Click on **Scratch 3**.
+
++ Read the instructions on the page to learn how to use machine learning blocks in Scratch.
+
++ Click on **Open in Scratch 3**.
+
+![annotation pointing at Open in scratch 3 button](images/open-scratch-3-annotated.png)
+
++ Click on **File** and then on **Load from your computer**, and select the Scratch project you saved earlier.
+
++ When Scratch asks you whether to replace the current project, click on **OK**.
+
+\--- /task \---
+
+\--- task \---
+
++ Click on the **Code** tab, and update your Scratch code to use your machine learning model **instead** of the rules you first added.
+
+The `recognise text … (label)` block is a new block added by your project. This new block can receive a message and return one of the four labels, based on the machine learning model you have trained.
+
+![New scratch code including new machine learning blocks](images/code-new-blocks.png) \--- /task \---
+
+\--- task \---
++ Click the **green flag** to test again.
+
+![Testing new code from previous instruction](images/test-with-new-blocks-annotated)
+
++ Test your project: type a command and press <kbd>Enter</kbd> on your keyboard. The fan or lamp should react to your command.
+
+Make sure you test that this works **even for commands that you didn’t include as examples in the buckets.**
+
++ Save your project: click on **File** and then on **Save to your computer**. \--- /task \---
+
+Now your Scratch smart virtual classroom uses a machine learning model instead of a rules-based approach.
+
+Using machine learning is better than using rules, because training a program to recognise commands for itself is much quicker than trying to make a list of every possible command.
+
+The more examples you use to train the machine learning model, the better your program should get at recognising commands.

af-ZA/step_7.md

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+## How to use confidence scores
+
+Finally, you will learn about what confidence scores mean and how you should use them.
+
+\--- task \---
++ Leave Scratch open, because you will come back in a moment.
+
++ Go back to the **Learn & Test** page in the Training tool.
+
++ Type something that has nothing to do with lamps or fans into the test box. For example, you could type in 'make me a cheese sandwich'. ![Result of entering "make me a cheese sandwich" is lamp off with 21% confidence](images/cheese-sandwich-annotated.png)
+
++ Look at the confidence score, which should be very low.
+
++ Compare this with the confidence score you get for a command such as “turn on the lamp”.
+
+**The confidence score is the program’s way of telling you how certain it is that it understands a command.** If a command is very similar to the examples you have trained the program with, the confidence score is high. If a command is **not** similar, the confidence score is low.
+
+\--- /task \---
+
+\--- task \---
+
++ Go back to your classroom assistant project in Scratch.
+
++ Modify the script for the 'classroom' sprite so that it uses the confidence score:
+
+![New code to be added into scratch program](images/code-with-confidence.png)
+
++ Click the green flag and test your program to check that your classroom assistant reacts in the right way:
+    + Type in commands that have nothing to do with the fan or lamp
+    + Ask for something to be turned on or off
+
+Now, if your program is not sure what you mean, it tells you so. Then you can try giving it another command. \--- /task \---
+
+You’ve used machine learning to train a smart assistant that is a simple version of the assistants you can get on smartphones (e.g. Apple’s Siri or Google’s Assistant) or at home (e.g. Amazon’s Alexa or Google’s Home).
+
+Training the program to recognise commands is much easier than trying to make a list of every possible command. And the more examples you give the program, the better it gets at recognising commands, and the more its confidence scores increase. 

af-ZA/step_8.md

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+## Challenge: more items to control
+
+\--- challenge \--- \--- task \---
+
+**Add another item**
+
++ In addition to a fan and a lamp, can you add another item and train your smart classroom assistant to understand your commands for controlling the item?
+
+\--- /task \---
+
+\--- task \---
+
+**Try our different confidence scores**
+
++ Is 70% the correct confidence score for deciding whether the smart classroom assistant has recognised a command correctly? Experiment with different confidence scores until you have a value that works well for your machine learning model.
+
+If you choose a number that is too high, the assistant will say “Sorry I’m not sure what you mean” too often.
+
+If you choose a number that is too low, the assistant will get too many things wrong. \--- /task \---
+
+\--- task \---
+
+**Real smart assistants**
+
+People have made [their own smart assistants based on Amazon’s Alexa](http://amzn.to/2sxy1hw){:target="_blank"}.
+
+People make these assistants the same way that you made yours:
+1. First, they create buckets for the types of commands they want their assistants to recognise
+1. Then they collect examples of how the commands might be phrased and trained the Alexa-based assistant to understand them
+
++ Find an Alexa Skill that you find interesting and look at the commands it can understand. How would you have trained this program?
+
+\--- /task \---
+
+\--- /challenge \---

af-ZA/step_9.md

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+## What next?
+
+If you haven't already, try our other machine learning with Scratch projects.
+
+[Journey to school](https://projects.raspberrypi.org/en/projects/journey-to-school)
+
+[Alien language](https://projects.raspberrypi.org/en/projects/alien-language)