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Someone who romanticises about the origins, the history. Teaching Enthusiast. Firmly believe in AI for social good.

| Chapter 1 — Introduction & Intuition

“Science is a Differential Equation.

Religion is a Boundary Condition” — Alan Turing

⏳“Time is but a stubborn illusion” — Genius

A bit nerdy, huh right? Apart from the evident metaphorical connection between Theology and Science, in a nutshell what the statement above…

A Structure from Chaos

2E=mc² being sold at $100.00 (Photographer)

DLA, Diffusion Limited Aggregation is nothing but particles taking a random walk (called Brownian Motion) within the canvas or a confined space in which the structure will be simulated, it could be a simple 2D canvas of pixels or a 3D volume where the structure is…

|Building a ML Pipeline : Part 5

Because sometimes the evaluation metrics wont be enough..

Until unless inherently interpretable models such as Linear Regression, Logistic Regression, GLM, GAM, Decision Tree etc. are used, interpreting a Black Box Model becomes a necessity for the built model to go into production.


Pearson Correlation is not one solution for all.

Different types of data demand different type of measure for association with the response variable.

FUN FACT : Somewhere out there in this vast multiverse, there might be a planet made of Diamond 💎as Yale University Research Suggest. Also there is water floating in the universe 140 trillion times the mass of water on earth some 10 billion light years away, Literally. We goin swimming 🏊(Photographer)

Before diving into the bi-variate association, its quite important to have a brief introduction to different types of the Statistical Data type that we deal with.

Different Types Of Data

Data (singular datum) — are individual units of information

|Building a ML Pipeline : Part 4

If not anything else, one thing that i have learnt that working through Machine Learning projects is that it becomes much more easier if there is a proper structure in place.

This is the Fourth article in the series Building a ML Pipeline…

|Building a ML Pipeline : Part 3

Selecting features from a plethora of variables can improve not just the performance but also reduce the training time substantially, and it becomes quite easier to interpret the model as well.

This is the Third article in the series Building a ML Pipeline…

| Building a ML Pipeline : Part 2

From reading the data to feature engineering, and doing some preliminary analysis along the way which generates some intuition about the data we would be dealing with.

This is the Second article of the Building a ML Pipeline series, in continuation to…

Building a Machine Learning pipeline, from scratch.

Quite a Swirly Pipeline! (Photographer)

Building a ML Pipeline : Part 1

Giving a structure to the entire process from reading the data and preparing the data to doing Exploratory Data Analysis, Feature Engineering, Feature Transformation, building Base Models and then eventually fine tuning the models through a…

Origins and Unfolding

Part 1 : The History & Basic Intuition

Time. An indefinite continued progress of existence and events that occur in apparently irreversible succesion from the past , through the present and into the future. Time is a component quantity of various measurements used to sequence events…

Time is a pretty…

Achintya Gupta

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