Episode
Understanding Linear Regression (Part 10 of 17) | Machine Learning for Beginners
with Bea Stollnitz
In this video, Bea Stollnitz, a Principal Cloud Advocate at Microsoft, helps you understand the concept of linear regression, a fundamental machine learning algorithm. 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 linear regression is and how it works
- How to interpret the parameters of a linear regression model
- The concept of least-squares regression
- How linear regression can be extended to multiple features
We'll start with a one-dimensional scenario, where we have a single feature x, and explain how linear regression finds the best line that approximates the general shape of a cloud of data points. We'll discuss the concepts of error minimization and the least-squares method. Then, we'll briefly touch on how linear regression can be extended to multiple features.
By the end of this video, you'll have a solid understanding of the core concepts behind linear regression, preparing you for the next video in our series, where we'll discuss correlation and its importance when training linear regression models.
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
- 00:13 - What is linear regression?
- 01:10 - Least squares regression
- 01:27 - Multidimensional linear regression for multiple features
- 01:52 - The mathematical function for 1 dimensional linear regression
Recommended resources
- 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
- Bea Stollnitz | Blog
- Bea Stollnitz | Twitter: @beastollnitz
- Bea Stollnitz | LinkedIn: in/beatrizstollnitz/
In this video, Bea Stollnitz, a Principal Cloud Advocate at Microsoft, helps you understand the concept of linear regression, a fundamental machine learning algorithm. 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 linear regression is and how it works
- How to interpret the parameters of a linear regression model
- The concept of least-squares regression
- How linear regression can be extended to multiple features
We'll start with a one-dimensional scenario, where we have a single feature x, and explain how linear regression finds the best line that approximates the general shape of a cloud of data points. We'll discuss the concepts of error minimization and the least-squares method. Then, we'll briefly touch on how linear regression can be extended to multiple features.
By the end of this video, you'll have a solid understanding of the core concepts behind linear regression, preparing you for the next video in our series, where we'll discuss correlation and its importance when training linear regression models.
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
- 00:13 - What is linear regression?
- 01:10 - Least squares regression
- 01:27 - Multidimensional linear regression for multiple features
- 01:52 - The mathematical function for 1 dimensional linear regression
Recommended resources
- 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
- Bea Stollnitz | Blog
- Bea Stollnitz | Twitter: @beastollnitz
- Bea Stollnitz | LinkedIn: in/beatrizstollnitz/