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Machine Learning in Python With Raspberry Pi & Sensors (Level 2)

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AI Code Academy
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In this 10 lesson program, students will learn the concept of Internet of Things (IoT) and how to control and communicate with various sensors using Python coding and Raspberry Pi

Class Experience

10 lessons//10 Weeks
 Week 1
Lesson 1
LCD Screen
Designed to provide students with an introduction to I2C communication, as well as experience in displaying information on an LCD Screen. Task: LCD Screen Implementation Assist students with I2C setup, circuit creation, and development of code which will allow the screen to display the current time and CPU temperature.
 Week 2
Lesson 2
AD/DA
Designed to provide students with a more in-depth view of various circuit components and I2C communication in conjunction with the concepts of potentiometers, Analog-to-Digital conversion, and Digital-to-Analog conversion. Task: Demonstrate AD/DA Assist students in the creation of the project circuit and development of code which will represent the AD/DA conversion process
 Week 3
Lesson 3
RGB LED
Instruct students on the concepts of the RGB lighting system as well as the circuit components used to generate various combinations of RGB lighting. Task: RGB LED Implementation Walk students through the programming and circuit configuration necessary in order to set up an RGB LED
 Week 4
Lesson 4
Thermistor
Walk students through the concept of heat-associated resistance and how it can be implemented on the Pi/Circuit. Task: Thermistor Setup Walk students through the programming and circuit configuration necessary in order to set up a thermistor within a circuit.
 Week 5
Lesson 5
Joystick and Snake
Provide students with an understanding of joystick control and how it can be implemented in code. Perform the circuit setup and code development necessary to monitor joystick positioning. Task: Snake Game Provide students with an introduction to more advanced applications of circuit components, through the use of the previous joystick circuit and the Pygame library. Walk students through the code required to make a fully functional game of Snake, which can be controlled by a joystick.
 Week 6
Lesson 6
Attitude Sensor
Provide students with an understanding of the attitude sensor component and its association with the Pi. Also, discuss new circuit component architectures. Task: Attitude Sensor Setup Instruct students to create the circuit and generate code in order to create a functional attitude sensor.
 Week 7
Lesson 7
Bar Graph LED
Introduce students to the concept of integrated circuits, serial-to-parallel data communication, and different types of LED components. Task: Bar Graph LED Setup Walk students through the programming and circuit configuration necessary in order to set up 8 LEDs lighting in sequence.
 Week 8
Lesson 8
7-Segment Display
Provide students with an understanding of how to use integrated circuits to light up multiple patterns of LEDs, forming letters and numbers. Task: 7-Segment Display Setup Assist students in developing the circuit and code required to make use of a 7-segment display.
 Week 9
Lesson 9
Matrix Keypad
Discuss the concept of inputs from circuit components, through the use of a keypad, into a Pi. Also discuss the anatomy of the keypad component. Task: Matrix Keypad Setup Perform the tasks necessary to create a functional keypad that works in coordination with the Pi
 Week 10
Lesson 10
Final Project
The students will create their own program applying what they have learned throughout the class. Upon completion, the projects are demonstrated by their creators.
  • Students will learn the concept of Internet of Things (IoT) and how to control and communicate with various sensors using Python coding and Raspberry Pi
2 - 4 hours per week outside of class
Projects
Frequency: 1-2 throughout the class
Feedback: included
Details: Projects are not mandatory but we strongly encourage students to complete them
Assessment
Frequency: included
Details:
Prerequisites: Python experience (15 hours at least) and Raspberry Pi experience (10 hours at least) required

Materials needed: we presume you already had the following hardware. If any parts are spoiled please repurchase from Amazon:

1, CanaKit Raspberry Pi 3 Kit with 2.5A Power Supply ($60)    https://www.amazon.com/CanaKit-Raspberry-Premium-Supply-Listed/dp/B01C6EQNNK/ref=sr_1_3?keywords=raspberry+pi+3&qid=1575431887&sr=8-3

2, 32GB microSDHC Class 10 microSD Memory Card ($9)    https://www.amazon.com/Kingston-32GB-microSDHC-microSD-SDCS/dp/B079GTYCW4/ref=sr_1_3?dchild=1&keywords=32GB+microSDHC+Class+10+microSD+Memory+Card&qid=1589205799&s=electronics&sr=1-3

3, Freenove Ultimate Starter Kit for Raspberry Pi 4 B 3 B+ ($50)     https://www.amazon.com/Freenove-Raspberry-Processing-Tutorials-Components/dp/B06W54L7B5/ref=sr_1_1?dchild=1&keywords=freenove+ultimate+starter+kit+for+raspberry+pi+4b+3+b%2B&qid=1589205731&s=electronics&sr=1-1

4, If your computer doesn't have a SDHC card port, you also need to purchase a Vanja Micro USB OTG Adapter   https://www.amazon.com/Vanja-Adapter-Portable-Memory-Reader/dp/B00W02VHM6/ref=sr_1_6?crid=27W57S4IKFGO9&dchild=1&keywords=sd+card+to+pc+adapter&qid=1591152969&sprefix=sd+card+to+pc%2Caps%2C149&sr=8-6
To join this course, students must have learned how to build physical models using Raspberry Pi and Python, or participated in our Machine Learning in Python with Raspberry Pi and Sensors. We presume you already had Raspberry Pi and Sensors and your Pi has been connected to your computer
Technical Requirements:

PC (Windows 10) or Mac (macOS 10.13)  with at least a 2GHz processor and 2GB of RAM (4GB of RAM is recommended).

No Chromebook!!!

Broadband internet with at least 1.8Mbps download and 900Kbps upload speeds. Please make sure to remove your firewall if any

Webcam - Either external or built-in (many laptops have an integrated camera).

Microphone and Speakers - We recommend headphones with an integrated microphone 
Joined April, 2020
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Project-based, STEM Coding + AI Programs + Mathematics

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Group Class

$289

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

Completed by 53 learners
Live video meetings
Ages: 11-15
3-8 learners per class

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