Intro to Machine Learning: Linear Regression using Scikit-Learn

Hello Everyone !!

Thanks for continuing with this post.

In the last post, we discussed about the code and working of Linear Regression with Multiple Variables using Gradient Descent, the theory and the mathematics behind it.

In this post, we will start writing the code for Linear Regression using Scikit-Learn. So, let's get started.

You can find the Python code file and the IPython notebook for this tutorial here.

Linear Regression using Scikit-Learn:

So, in this tutorial we got an insight into the working of Scikit-Learn for Linear Regression. Some more widely used tools by companies for Machine Learning and Deep Learning are Tensorflow, Mxnet, Keras etc.

In the next posts we'll have a look at the working of these tools for Linear Regression and see the differences in the previous approaches that we have studied so far.

Now that we have covered Linear Regression using Scikit-Learn, let's move to our next implementation, i.e. Linear Regression using Tensorflow.

Great work on completing this tutorial, let's move to the next tutorial in series, Introduction to Machine Learning: Linear Regression using Tensorflow

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