ml-302: Intuition for Linear Regression
Hello! Welcome back. Last time we studied the "univariate linear regression" ML algorithm here. That time we took in a lot of new concepts, and not everything might be clear to all. So today we will dig a bit deeper into the various components and try to understand how and why they work correctly. This will help us analyze if our implementation is working correctly and also to debug things if it is not.
So lets begin with a quick review. First, the terminology
Continue reading →Golbin updates: bug fixes and new features
Hi! We hope that you are liking the various stories that you see here on Golbin. We are always striving not only to bring you quality content that will interest you, but are also adding new features to the site. As we have mentioned previously here, here, here and here, we have regularly added new features, and we sincerely hope that you like them :)
In today's release we have made the following changes:
Continue reading →ml-301: Linear Regression with one variable
Welcome back! After having gone through some posts on introduction to ML (here, here, here, here and here), lets now start learning a real ML algorithm. The algorithm that we will study today is a supervised learning regression algorithm.
First lets define the problem statement. Today we will use a subset of thisdata. The first few training examples in the data are:
Continue reading →ml-202: octave -- part - 2
Hi there, welcome back! In the last post, we looked at some basic octave commands. Today we will dig deeper into octave and understand more concepts and commands. I hope you are eager and excited, so lets begin!
Plotting: One of the most important things that we do during implementing ML algorithms is to plot data. This is done to
Continue reading →ml-201: octave -- part - 1
Hi again! In the previous posts here, here and here, we did pretty theoretical and very basic introduction. But starting now (notice the 201 in the title of the article?), we will be digging deeper into specific algorithms, their working, intuition and implementation. But before we go there, we need to get familiar with some background setup.
Firstly, you should have atleast some basic knowledge of the following disciplines of mathematics:
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