This article is just to have an easy access to the posts I have written in the past. Blogs in different domains would be segmented in different sections.

Data Science — General

  1. The Tradeoff Between Prediction and Interpretation.

It’s All about regression (Series)

  1. Cost Functions and Gradient Descent
  2. Summary Table on Share Prices
  3. Inferential Data Analysis (coming soon)

Amazon Web Services (Series)

Project (Series)

Incase you have any suggestions on topics you’d want me to cover in my future blogs feel free to drop a comment. :)

Keep Rocking!


In our last blog post, we learnt about ordinary least squares (OLS) for optimising a linear regression model. We covered the basics of linear regression model, skimmed through the idea of cost function & gradient descent and created our very first linear regression model by using python package statsmodel. Finally we ended the post by discussing the assumptions under which the model operates. In this blog post we’ll dive deeper into cost functions and gradient descent and try to understand how the OLS uses it to come up with optimal beta estimates for a model.

Cost Functions

Suppose you buy a packet…

Photo by Richard Lee on Unsplash

Ever since machine learning or artificial intelligence took a leap in popularity, more often than not, we think of M.L. as a piece of software/code that has the capabilities of predicting the future given an input data. For instance, forecasting the price of bitcoin or recommending a user with products he/she is most willing to buy. As fascinating as it may sounds to have such capabilities one of the down side of it is that practitioners new to M.L. use it like a black box and hence do not make use of its full potential.

In this blog, I will…

Photo by Joshua Sortino on Unsplash

In this blog we are going to focus on creating a data warehouse on AWS Redshift and establishing a connection to it from our local machine.

Note : This blog is going to cover an introductory level information of creating a cluster and getting started with writing basic queries. It intends to be a one stop point to start your journey towards data warehouse.

Amazon Redshift

Well, before we jump onto implementation it is always a good idea to understand the technology we intent to use. …

Linear Regression is more than just a predictive model. When used correctly, it could help you not only solve complex problems but also can help you with some inferential analysis. Before we start let us first recap what we have covered so far.

Until now, in the series “It’s All About Regression”, we have looked into

  1. Linear Regression Models : We discussed about linear regression and implemented a very basic regression model using Ordinary Least Squares ( OLS ). Furthermore we discussed a few assumptions under which the model operates.
  2. Gradient Descent : We looked into various cost optimisation functions…

In this series consisting of multiple blogs we are going to look into regression models. This is going to be different that a lot of other regression tutorials / lectures you might have seen online as we will cover the topic from multiple angles

What sets this series apart from the rest out there
- Real world examples and application
- predictive analysis
- inferential analysis (something almost always forgotten)
- furthermore, we’ll look into regression beyond Ordinary Least Squares (OLS)

So without any further ado, let us get started.

Linear Regression

A linear regression is a statistical model that maps a…

Being a data science enthusiast we come across hundreds of problems from random normal distributions to random poisson distribution, from random sampling to random outcomes and while studying so much of randomness throughout the course I realised that randomness has been a part of our curriculum since the very beginning of life. It goes from an arbitrary numbers in a geometric plane to a random number in a magicians play and this unpredictability in the surrounding excites me to think if randomness is truly that random or is it just that math we are not yet ready for.

Keeping this…

Vachan Anand

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