Episode

Looking for Correlation: The Key to Linear Regression (Part 11 of 17) | Machine Learning for Beginners

with Bea Stollnitz

Machine Learning for Beginners

In this video, Bea Stollnitz, a Principal Cloud Advocate at Microsoft, explains the concept of correlation and how it's essential for successful linear regression predictions. This video is part of our Machine Learning for Beginners series, where we cover various machine learning topics and their implementation using Python code in Jupyter notebooks.

In this video, you'll learn:

  • What correlation is and how it's measured
  • How to evaluate the correlation between two variables using scatter plots
  • How to calculate correlation values using code
  • The importance of correlation for linear regression predictions

We'll walk you through the process of visualizing the correlation between variables using scatter plots and calculating correlation values using Python code. By the end of the video, you'll understand the importance of strong correlation in making accurate predictions with linear regression and how to identify potential correlations in your data.

Stay tuned for the next video in this series, 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!

Chapters

  • 00:00 - Introduction
  • 00:15 - What is correlation?
  • 00:34 - calculation correlation using the corr function
  • 00:42 - Making sense of correlation results, and positive and negative correlation
  • 01:22 - Calculate correlation in code
  • 01:58 - Check correlation with a scatter plot
  • 02:20 - Check correlation using code
  • 02:29 - Look for patterns in the scatter plot
  • 03:04 - Look for correlation using filtered data
  • This course is based on the free, open-source, 26-lesson ML For Beginners curriculum from Microsoft.
  • The Jupyter Notebook to follow along with this lesson is available!

Connect

Azure Machine Learning
Python