Category: Supervised Learning

Math Heavy Topics in Data Science!￼
1. Supportvector machine Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a nonprobabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic […]

Interview with a Kaggle Master & More
1. Exclusive Interview with 2x Kaggle Master Gilles Vandewiele! “I think one of the nice things about the data science field is that it is so multidisciplinary and that anyone who aspires to become a data scientist can do so.” – Gilles Vandewiele Golden words! As a beginner in data science, this quote gives me […]

Resources to learn Linear Regression
Linear regression shows the linear relationship between the independent(predictor) variable i.e.Linear regression is a quiet and the simplest statistical regression method used for predictive analysis in machine learning. How a Math equation is used in building a Linear Regression model? Do you know that this one equation helps in building a linear regression model in the machine learning world? Yes, you heard it right.From the school days, we have come across the equation of the straight line.

Natural Language Processing Usecases with Python
1. Master Natural Language Processing in 2022 with Best Resources As already mentioned earlier, Deep Learning is a subdomain of machine learning. It is far more generalized as it comes up with generalized predictions compared to traditional machine learning due to the introduction of Artificial Neural Networks or ANN. Practicing NLP with Deep Learning is an […]

Decision Tree From Scratch!! Part I
Introduction In this blog post, I am going to talk about a powerful supervised learning algorithm that is often used in Machine Learning competitions. It is called the Decision Tree algorithm. It can be used for both classification & regression tasks. In this post, I will discuss the need for treebased algorithms, the basics of […]

Logistic Regression in Machine Learning (from Scratch !!)
Introduction In this blog post, I would like to continue my series on “building from scratch.” I will discuss a linear classifier called Logistic Regression. This blog post covers the following topics, Basics of a classifier Decision Boundaries Maximum Likelihood Principle Logistic Regression Equation Logistic Regression Cost Function Gradient Descent Algorithm After the discussion of […]

IPL Score Prediction Using Machine Learning
Introduction With the IPL season coming up (for those who are not familiar with IPL, it’s the EUROPA League or the NBA of Cricket in India and all the cricketing nations) I wanted to share the use case of Data Science in cricket. Data Science and Analytics are being used in Sports extensively. You can […]

KNN in Machine Learning (from Scratch !!)
KNearest 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 KNearest Neighbours. It is a simple, easytoimplement supervised machine learning algorithm that can be used to solve both classification and regression problems. Note: […]

Heteroscedasticity
In this post, I want to talk about Heteroscedasticity. I can understand, some of you might have a hard time pronouncing it (atleast 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, […]