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Coding up a Linear Regression Algorithm from Scratch


Whether we learn data science through online courses, tutorials, or jump straight into a hands-on project-based approach, few of us take out the time to try and learn how some of our favorite libraries are built. All we know is that we can import the scikit-learn library, instantiate a Linear Regression model, and call the “.fit()” method on it. What is happening under the hood? How are these different algorithms implemented in an efficient manner? 

We’ll walk you through an advanced Python tutorial where we will be coding a Linear Regression algorithm from scratch and make it usable.

This webinar is not for the Python beginner, you are expected to know basic data science tools and frameworks, such as Pandas, Numpy, Scikit-Learn, etc. You are also expected to know, at least in theory, how Linear Regression works. This webinar will bridge that gap between theory and implementation and in the process teach you some advanced Python tips and tricks.

What you’ll learn

  • Object-Oriented Programming in Python
  • Vectorized Operations
  • Efficient coding strategies
  • Conversation of mathematical formulas into Python code
Asim Waheed
Asim Waheed

Data Scientist at Data Science Dojo

Asim holds a bachelor’s degree in Computer Science. He is a Data Scientist at Data Science Dojo where he works on building data products while consulting Fortune 500 companies, and course development for online bootcamps as well as corporate training. Before working as a Data Scientist, he was a Research Assistant at the Security and Internet Analytics lab at Lahore University of Management Sciences, working mostly on problems related to Deep Learning Security, and Internet Analytics. He has worked in collaboration with the Machine Learning Security group in Virginia Tech.

We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!


Notebook and files can be downloaded using the below links: