• # KNN in Machine Learning (from Scratch !!)

K-Nearest Neighbours: Introduction Birds of a feather flock together. William Turner The above quote perfectly sums up the algorithm that we are going to talk about in this post. KNN stands for K-Nearest Neighbours. It is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. Note: […]

• # Data Science Starters

So, this post is for someone who wants to learn Data Science but is not able to find where to start from. In this post, I will share some crucial resources (free) that would help you get started with your Data Science journey, Programming So, to get started with Data Science you must be fluent […]

• # Heteroscedasticity

In this post, I want to talk about Heteroscedasticity. I can understand, some of you might have a hard time pronouncing it (at-least I did). Anyway, we are more interested in its meaning and it’s application rather than its pronunciation. So, Heteroscedasticity means the variability of a random disturbance is different across a vector. Now, […]

• # Linear Regression in Machine Learning (from Scratch !!)

Introduction In this post, I will talk about one of the most crucial techniques in Regression Analysis/Machine Learning, called Linear Regression. As per Wikipedia, Regression Analysis is defined as a set of statistical processes used to estimate the strength of the relationship between a dependent variable and an independent variable. The process which tries to […]

• # How to achieve multiple offers in Data Science as a College Student…

Introduction First of all, I would like to introduce myself. My name is Chitwan Manchanda and I am currently pursuing B.Tech in Mechanical Engineering from Delhi Technological University. I have offers from Simpl, Editorialist YX and Slice for Data Science roles. I was in my penultimate year when the Covid-19 Pandemic hit and we all […]

• # Hypothesis Testing

In statistics, hypothesis testing is a form of inference using data to draw certain conclusions about the population. First, we make an assumption about the population which is known as the Null Hypothesis. It is denoted by H₀. Then we define the Alternate Hypothesis which is the opposite of what is stated in the Null Hypothesis, denoted by Hₐ. After defining […]