Scribd
Upload
59 views3 pages

Worksheet 2

This document provides prompts for reflections on key concepts in machine learning projects. Trainees are asked to consider: 1) The appropriate machine learning tasks (regression, classification, clustering) for real-world applications they find interesting. 2) For their favorite application, the problem being solved, key stakeholders, and how machine learning improves the existing solution. 3) A specific, quantifiable "win condition" deliverable for their chosen application. 4) The relevant target variable and 3 potential input features for a model.

Uploaded by

Joe1
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
59 views3 pages

Worksheet 2

This document provides prompts for reflections on key concepts in machine learning projects. Trainees are asked to consider: 1) The appropriate machine learning tasks (regression, classification, clustering) for real-world applications they find interesting. 2) For their favorite application, the problem being solved, key stakeholders, and how machine learning improves the existing solution. 3) A specific, quantifiable "win condition" deliverable for their chosen application. 4) The relevant target variable and 3 potential input features for a model.

Uploaded by

Joe1
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
You are on page 1/ 3

REFLECTIONS WORKSHEET 

Anatomy of a Machine Learning Project 


 
These worksheets are meant to encourage you to actively engage with the core concepts 
taught in this program. While the majority of the program will be focused on code, 
implementation, and practical skills, these worksheets give you the opportunity to reflect, 
brainstorm, and fill in any conceptual gaps. 
 
Reflection #1: Machine Learning Tasks 
In the previous worksheet for ​Wisdom from the Trenches​, you listed 3 real-world 
applications of machine learning that you personally found interesting. Now, based on 
what you learned in this module, write down what you think is the appropriate m​ achine 
learning task​ for each one. Is it regression? How about classification? Or maybe 
something in unsupervised learning, such as clustering? 
 
 
 
Reflection #2: Three Questions Before You Start 
Pick your favorite real-world machine learning application that you brainstormed in the 
previous worksheet. Assume you are working on that application. Based on what you 
currently know, answer the following 3 questions to the best of your abilities. 
 
What is the ​problem y​ ou are trying to solve? 
 
 

 
Who are the ​key stakeholders? 
 
 

 
 
What does machine learning offer over the ​existing solution? 
 
 

 
 
 
Reflection #3: Defining the Win Condition 
For the example you chose for Reflection #2, based on your answers above, write down 
what you think would be a reasonable win condition or deliverable. Remember, try to 
make it ​specific​ and q
​ uantifiable​. For example, “consistently predict house prices within 
$Y​ of actual prices” is better than “predict housing prices.” 
 
 

 
Reflection #4: Data Collection 
Now that you’ve learned about the idea of a ​target variable​ and i​ nput features​, write 
down what you think would be the target variable you’d need (if applicable). Then, 
brainstorm 3 ideas for relevant input features that you think would help your model. 
 
 

You might also like