Machine Learning for Beginners - Understanding Logistic Regression for Machine Learning Classification [Part 14] | ML for Beginners

Welcome to another insightful video presented by Bea Stollnitz, a Principal Cloud Advocate at Microsoft. In this video, we'll dive into the world of logistic regression, learn how it compares to linear regression, and explore its applications in classification tasks, including how it can make better predictions against our pumpkin data set 🎃. What you'll learn: ✅ The difference between linear and logistic regression ✅ Binary logistic regression and the use of the sigmoid function ✅ Multinomial and ordinal logistic regression Join Bea as she unravels the fascinating world of logistic regression, and learn how it can be utilized in classification problems. This video is perfect for those who want to expand their understanding of regression techniques and enhance their machine learning skill set. Make sure to subscribe and hit the notification bell 🔔 so you won't miss our next set of videos, where you'll be writing code to apply the theory you've learned. See you there! 📙 Follow along: The Jupyter Notebook to follow along with this lesson is available here: https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression/4-Logistic #Python #ScikitLearn #pandas #LogisticRegression #DataScience #MachineLearning #ml 📚 Learn more: This course is based on the free, open source, 26 lesson ML For Beginners curriculum from Microsoft, which can be found at https://aka.ms/ml-beginners. 📇 Connect with Bea: Blog: https://bea.stollnitz.com/blog/ LinkedIn: https://www.linkedin.com/in/beatrizstollnitz/ Twitter: https://twitter.com/beastollnitz 0:00 - Intro 0:37 - Linear regression vs binary logistic regression 1:23 - Threshold values 1:53 - Logistic regression for pumpkin colors 2:20 - Multinomial and ordinal logistic regression