Patch 1

NOTICE

@@ -4,9 +4,6 @@ All Rights Reserved.
 Licensed under the LinkedIn Learning Exercise File License (the "License").
 See LICENSE in the project root for license information.
 
-ATTRIBUTIONS:
-[PLEASE PROVIDE ATTRIBUTIONS OR DELETE THIS AND THE ABOVE LINE “ATTRIBUTIONS”]
-
 Please note, this project may automatically load third party code from external 
 repositories (for example, NPM modules, Composer packages, or other dependencies). 
 If so, such third party code may be subject to other license terms than as set 

README.md

@@ -1,7 +1,10 @@
-# Machine Learning with Python: Logistic Regeression
-This is the repository for the LinkedIn Learning course `Machine Learning with Python: Logistic Regeression`. The full course is available from [LinkedIn Learning][lil-course-url].
+# Machine Learning with Python: Logistic Regression 
+This is the repository for the LinkedIn Learning course Machine Learning with Python: Logistic Regression. The full course is available from [LinkedIn Learning][lil-course-url].
+
+![1666987773227](https://user-images.githubusercontent.com/25848438/200744832-b3c06517-af4a-4dea-9131-fc05d33e866f.jpeg)
+
+Are you looking for a practical way to use machine learning to solve complex real-world problems? Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a step-by-step guide on how to build a logistic regression model using Python. Learn hands-on tips for collecting, exploring, and transforming your data before you even get started. By the end of this course, you’ll have the technical skills to know when and how to design, build, evaluate, and effectively manage a logistic regression model all on your own.<br><br>This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the [Using GitHub Codespaces with this course][gcs-video-url] video to learn how to get started.
 
-_See the readme file in the main branch for updated instructions and information._
 ## Instructions
 This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add `/tree/BRANCH_NAME` to the URL to go to the branch you want to access.
 
@@ -20,15 +23,12 @@ To resolve this issue:
     Add changes to git using this command: git add .
 	Commit changes using this command: git commit -m "some message"
 
-## Installing
-1. To use these exercise files, you must have the following installed:
-	- [list of requirements for course]
-2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
-3. [Course-specific instructions]
+### Instructor
 
+Frederick Nwanganga
 
-[0]: # (Replace these placeholder URLs with actual course URLs)
+Check out my other courses on [LinkedIn Learning](https://www.linkedin.com/learning/instructors/frederick-nwanganga?u=104).
 
-[lil-course-url]: https://www.linkedin.com/learning/
-[lil-thumbnail-url]: http://
+[lil-course-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression
+[gcs-video-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression/using-github-codespaces-with-this-course