Week 1
Lesson 1
Overview
Designed to provide students with a very basic understanding of machine learning concepts that they will be able to apply in the course. They will also be shown the tools they will be using throughout the course.
Task: Set up AICode101 Accounts
Students will be tasked with creating AICode101 accounts and walking through the website to learn how it will be used in the course.
 Week 2
Lesson 2
Image Recognition Project with AICode101
Designed to provide students with an introduction to image recognition in machine learning. Students will be able to apply concepts in machine learning to generate their own program to recognize categorized images.
	Task: Image Recognition Project
	Students will be tasked with creating an AICode101 image recognition project. Through this, they will learn key fundamentals of image recognition in machine learning.
 Week 3
Lesson 3
‘Rain in Australia’ Number Project with AICode101
Designed to provide students with an introduction to classification with data in machine learning. Students will be able to apply concepts in machine learning to generate their own program to predict weather.
	Task: Number Project
	Students will be tasked with creating an AICode101 number project. Through this, they will learn key fundamentals of classification in machine learning.
 Week 4
Lesson 4
The Stock Market Fundamentals
Designed to provide students with an introduction to the stock market in preparation for the next lesson. Students will learn about what the stock market is and how it can be incorporated into machine learning.
	Task: Stock Market Search
	Students will be tasked with navigating financial sites to view different stocks and the associated data that defines a stock and its performance.
 Week 5
Lesson 5
Stock Price Prediction with AICode101
Designed to provide students with an introduction to data manipulation for use in machine learning and functional data classification through the use of models.
	Task: Data Analysis and Cleanup
	Students will be given data to analyze for important features and “clean up” the data to ensure no issues in calculation or model generation.
	Task: Model Generation
	Students will generate models using the data they have handled to make predictions.
 Week 6
Lesson 6
Flower Classification Project with Google Colaboratory
Designed to provide students with an introduction to machine learning in the Python programming language. Students will learn how to read data, generate models, and make predictions using the language and the “Google Colaboratory” program.
	Task: Data Analysis and Cleanup
	Students will be given data to load into their “notebooks” that can then be cleaned up, graphed, and analyzed.
	Task: Model Generation and Analysis
	Students will generate models to classify species of flowers as a basic examp
 Week 7
Lesson 7
Stock Market Price Prediction with Google Colaboratory
Designed to provide students with a more advanced understanding of machine learning fundamentals learned throughout the course. They will be able to apply the fundamentals to gain a deeper understanding of machine learning and its applications.
	Task: Data Analysis and Cleanup
	Students will be given data to load into their “notebooks” that can then be cleaned up, graphed, and analyzed.
	Task: Model Generation and Analysis
	
 Week 8
Lesson 8
NBA Draft Pick Prediction with Google Colaboratory
Designed to provide students with a more advanced understanding of machine learning fundamentals learned throughout the course. They will be able to apply the fundamentals to gain a deeper understanding of machine learning and its applications.
	Task: Data Analysis and Cleanup
	Students will be given data to load into their “notebooks” that can then be cleaned up, 	graphed, and analyzed.
 Week 9
Lesson 9
Class Review with Kahoot 
Students will review all of the material learned in the course in a fun review class. The Kahoot quiz software will be used to provide a fun, interactive experience for students.
 Week 10
Lesson 10
Final Project Presentations
Students will present their final projects based off of data provided to them in a previous class. They will have time to work on their projects at the beginning of class.