Machine Learning for Beginners - Your First Linear Regression Project in Python [Part 7] | Machine Learning for Beginners

📈🐍 In this tutorial, presented by Bea Stollnitz, a Principal Cloud Advocate at Microsoft, we'll guide you through creating your first linear regression project using Python and a toy dataset from scikit-learn. This video is part of our Machine Learning for Beginners series, where we'll cover various machine learning topics and their implementation using Python code in Jupyter notebooks. In this video, you'll learn: ✅ How to load the diabetes dataset from scikit-learn ✅ How to extract and reshape the relevant data (BMI) for our regression model ✅ How to split the data into training and testing sets ✅ How to create and train a linear regression model using scikit-learn ✅ How to make predictions and evaluate the model's performance using matplotlib If you're new to Jupyter notebooks and virtual environments, make sure to watch our previous video, where we walk you through setting up a Jupyter notebook with a virtual environment for data science projects. Make sure to subscribe and hit the notification bell 🔔 so you won't miss our upcoming videos, where we'll dive deeper into various machine learning topics and guide you through their implementation using Python code in Jupyter notebooks. 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/1-Tools #LinearRegression #Python #ScikitLearn #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 0:21 - Open the Jupyter notebook from the ML for beginners repo 0:50 - The diabetes toy dataset from SciKit-Learn 1:36 - Load the diabetes dataset using the notebook 2:30 - Reshape numpy arrays to format the diabetes data 2:49 - Split data into train and test data sets 3:14 - Create and train a linear regression model, and make predictions 3:35 - Use matplotlib to visualize the predictions