Hello Everyone !!
Thanks for continuing with this post.
In the last post, we discussed about the code and working of Linear Regression using Gradient Descent, the theory and the mathematics behind it.
In this post, we will start writing the code for Linear Regression with Multiple Variables using Gradient Descent. So, let's get started.
You can find the Python code file and the IPython notebook for this tutorial here.
Linear Regression with Multiple Variables using Gradient Descent:
So, this covers all the code about which we have been talking in the past discussions.
These codes give us a deep insight into the working of the various techniques. But in production, we usually need to work with the given tools as they are much better optimized and perform better than these codes.
So, I would also like to introduce a very famous and widely used tool in the Industry for Machine Learning called as Scikit-Learn. Scikit-learn is a Machine Learning library that provides access to almost all the algorithms in a very precise and optimized form.
Hence, in the next post, we will discuss how to use scikit-learn for Linear Regression.
Now that we have covered Linear Regression with Multiple Variables using Gradient Descent, let's move to our next implementation, i.e. Linear Regression using Scikit-Learn.
Great work on completing this tutorial, let's move to the next tutorial in series, Introduction to Machine Learning: Linear Regression using Scikit-Learn
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