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Machine Learning Projects With Scratch Coding (Level 4)

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AI Code Academy
Average rating:
4.7
Number of reviews:
(1,663)
Popular
In this 10-lesson online course, students will have hands-on experience training machine learning models to recognize sound, images, and text, and will apply the trained models using Scratch to create impressive projects.

Class Experience

10 lessons//10 Weeks
 Week 1
Lesson 1
Introduction to AI and Machine Learning
Students will be introduced to the concept of artificial intelligence. Topics covered: ○ What is AI? ○ History of AI ○ Examples of AI ○ How do machines learn? ○ Creating image recognition machine learning models Project: Build a machine learning model to detect whether you play rock, paper, or scissors using image recognition.
 Week 2
Lesson 2
Pet Commands with voice recognition
Students will learn to create data-based machine learning models using voice recognition to command animated pets in Scratch. Topics covered: ○ Creating sound recognition machine learning models ○ Recording voice clips for training data ○ Using Scratch to listen for voice commands Project: Make an animated pet follow verbal commands and do tricks using a sound recognition machine learning model.
 Week 3
Lesson 3
Smart Light
Students will learn to create and apply text recognition machine learning models, and compare machine learning solutions with rule-based solutions. Topics covered: ○ Creating rule-based text matching models ○ Creating data-based machine learning models ○ Comparison of rule-based and data-based solutions ○ Discussion of applications of machine learning for smart home devices Project: Create a smart light in Scratch that turns on when a command is given using a text recognition model.
 Week 4
Lesson 4
Emotion Recognition
Students create models that can recognize emotions of faces using image recognition. Topics covered: ○ Creating image recognition models ○ Represent confidence of prediction visually in Scratch ○ Understand the importance of quality training data Project: Train an image recognition machine learning model to recognize emotions in drawings of faces and use Scratch to display the confidence of results.
 Week 5
Lesson 5
Ask an Animal:
Students will create an animal chatbot that answers questions about a topic they choose. Topics covered: ○ Creating rule-based text matching models ○ Creating data-based machine learning models ○ Comparison of rule-based and data-based solutions ○ Creating chatbots that respond to natural speech Project: Create an animal chatbot that responds intelligently by using text recognition machine learning to understand the question that was asked.
 Week 6
Lesson 6
Riddle Master
Students create a game in which you have to respond correctly to riddles to progress and use sound recognition to detect correct answers. Topics covered: ○ Creating sound recognition machine learning models ○ Recording voice clips for training data ○ Using Scratch to listen for only one answer at a time in a series of questions Project: Create a puzzle game that requires you to answer a series of riddles correctly and in the right order in order to reach the treasure.
 Week 7
Lesson 7
How Are You Doing
Students learn how to do basic sentiment analysis, and compare rule-based methods with data-based methods of recognizing emotions in text. Topics covered: ○ Creating rule-based text matching models ○ Creating data-based machine learning models ○ Comparison of rule-based and data-based solutions ○ Creating characters that respond to emotions in natural speech Project: Create a character in Scratch that understands how you are feeling based on input text and displays the same emotion.
 Week 8
Lesson 8
Maze Game
Students will compete to “train” a mouse to finish a maze the fastest using only voice commands and sound recognition machine learning models in Scratch. Topics covered: ○ Creating sound recognition machine learning models ○ Recording voice clips for training data ○ Using Scratch to listen for one of several commands, and complete the action before moving on ○ Optimizing the performance of a machine learning model
 Week 9
Lesson 9
Face Unlock
Students simulate the face unlock biometric security feature in some common devices by training an image recognition model. Topics covered: ○ Creating image recognition models ○ Understand how face unlock technology applies machine learning Project: Train an image recognition machine learning model to recognize your face in an image and apply this to a mock face unlock app in Scratch.
 Week 10
Lesson 10
Final Project
Students demonstrate what they’ve learned by sharing their creation with the class. They can get feedback from peers and the instructor, and get suggestions on how to make the project even better.
  • Demonstrated skills upon graduation: Solid understanding of supervised machine learning using labels and sample data Searching and sorting algorithms in Scratch Abilities to interpret the machine learning results Enhanced coding skills with loops, variables, and conditionals Improved problem-solving skills with problems from the real world
0 - 1 hours per week outside of class
Homework
Frequency: included
Feedback: included
Details: Students will create an individual final project of their own design. They can spend as much time as they'd like outside of class, but most do not take more than 1 - 4 hours.
Assessment
Frequency: included
Details:
Technical Requirements:
PC (Windows 10) or Mac (macOS 10.13) or ChromeOS with at least a 2GHz processor and 2GB of RAM (4GB of RAM is recommended).
Broadband internet with at least 1.8Mbps download and 900Kbps upload speeds
Webcam - Either external or built-in (many laptops have an integrated camera).
Microphone and Speakers - We recommend headphones with an integrated microphone 
In addition to the Outschool classroom, this class uses:
Dear Parents

Welcome to our Machine Learning With Scratch Coding for Kids. In this online live course, students will have hands-on experiences for training AI machine learning systems and building things with them, by creating projects and games with Scratch using text, image, or sound recognition. All lessons are project based and each lesson contains one fun project. All students will be asked to finish a final project with their own ideas which will be presented on the last day. There will be some quizzes for students to finish at home. 

To get started, please create an account at https://aicode101.com, which we will be using both in and out of class for this course.

Important notes:
As per Outschool Policy: For the safety of all students, all cameras must be on during the entire class duration, or at least for the first few minutes of the class session in certain circumstances. Please mute your audio (keep video enabled) during the class session.

For those that have never used Outschool before, please see this article on how to prepare for your first class:
https://support.outschool.com/en/articles/802757-how-to-prepare-for-your-first-class
To join the class, please login to your Outschool account and click the scheduled session to get into the classroom, where you click “Join live meeting” to join the zoom classroom. There is no zoom link needed.

If you have any questions/concerns please feel free to reach out through us on Outschool. We are beyond excited to have this opportunity to go on this amazing coding adventure with you all. Please come prepared and ready to learn for our first class, see you soon!

Best regards,
Delaware STEAM Academy

Joined April, 2020
4.7
1663reviews
Popular
Profile
Teacher expertise and credentials
Project-based, STEM Coding + AI Programs + Mathematics

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Reviews

Group Class

$199

for 10 classes
1x per week, 10 weeks
60 min

Completed by 293 learners
Live video meetings
Ages: 9-11
4-8 learners per class

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