Black Friday — A Detailed Analysis & Prediction using Visualization and XGBoost.

Black Friday — A Detailed Analysis & Prediction using Visualization and XGBoost. Nand Lal Mishra.

Launching the 2nd Edition of the Data Science Blogathon

An Unmissable Chance to Write and Win Lucrative Prizes! We are delighted to announce the launch of Analytics Vidhya’s second Data Science Blogathon, the ultimate competition which combines your writing prowess with your machine learning skills!

Machine Learning Clustering Techniques

Machine Learning Clustering Techniques. In this article, using Data Science , I will define basic of different types of Clustering algorithms.

Image Classification Using ANN.

In this blog we will be doing a project based on image classification where our problem statement describe us to classifies the images into two categories i.e. Emergency & Non-Emergency vehicle which is a binary classification problem and we will be solving using neural network.

Understanding Regularization Algorithms

Understanding the use of Regularization algorithms like LASSO, Ridge, and Elastic-Net regression. Before directly jumping into this article make sure you know the maths behind the Linear Regression algorithm. If you don’t, follow this article through!

The SVM we need to know || The SVM we implemented.

This post is very much in continuation to that, as here we will be discussing one more algorithm which we used in our Custom Pipeline, SVM(Support Vector Machines).

10 things I wish I’d known before starting as a Data Scientist

10 things I wish I’d known before starting as a Data Scientist. I was just a computer scientist. I’m being asked by students for advice on the subject so here are a few of my opinions.

RegEx in R for Data Science

RegEx in R for Data Science. The ‘regex’ family of languages and commands is used for manipulating text strings. More specifically, regular expressions are typically used for finding specific patterns of characters and replacing them with others.

What is this AI & ML? And what do I need to know to learn ML?

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart “

Object reference model in Python — a conceptual understanding.

Assumption before you start is — that you understand below concepts, if not — its worth a quick detour to below links: What does python do when you ask it to run a script? What are objects in python? How are these objects created and referenced?

The Case of the Missing Declaration Statements in Python

It’s important yet sometimes difficult to get a handle on what may be the most fundamental idea in Python programming and is certainly the basis of much of both the conciseness and flexibility of the Python language- dynamic typing, and the polymorphism it implies. This is a slightly long topic, but i wanted to explain this in depth as this is core to understanding Python.

Quick Python Tips to Explore Data

Simplest and quickest ways to do Exploratory Data Analysis with Pandas. I have taken Nobel Laureate dataset from Kaggle to do this exploratory data analysis, you can find the dataset here.

Implementation of Stochastic Gradient Descent

The purpose of writing this post is to understand the maths behind gradient descent. Most of us are using gradient descent in machine learning, but we need to understand the maths behind it. As a fresher, when I was learning stochastic gradient descent, I found it a little bit complex. Here, I tried to make it simpler for those who want to know how it works. My focus on this post is to demonstrate the mathematics behind gradient descent.

Ordinary Least Square (OLS) Method for Linear Regression

This post is about the ordinary least square method (OLS) for simple linear regression. If you are new to linear regression, read this article for getting a clear idea about the implementation of simple linear regression. This post will help you to understand how simple linear regression works step-by-step. The simple linear regression is a model with a single regressor (independent variable) x that has a relationship with a response (dependent or target) y that is

Vanishing and Exploding Gradient Problems

Vanishing and Exploding Gradient Problems: One of the problems with training very deep neural network is that are vanishing and exploding gradients.

SQL joins — A refresher

A non-tech guy’s way of learning data science. I’m writing this post as a part of my journey with MySQL and since joins is a confusing thing in the SQL, I’m explaining this by simplest terms as possible.

Machine Learning: Support Vector Regression (SVR)

In this blog, we’ll cover another interesting Machine Learning algorithm called Support Vector Regression(SVR). But before going to study SVR let’s study about Support Vector Machine(SVM).

Machine Learning: Logistic Regression

In this Machine Learning tutorial, we’ll learn any Machine Learning algorithm called Logistic Regression. If we go by the name then it seems that it is similar to linear regression but there’s a difference. Linear Regression is used to predict a continuous value based on certain features i.e. it’s a regression algorithm but Logistic Regression is a classification algorithm.

An Introduction of Degrees of Freedom In Machine Learning and Statistics.

Importance of Degrees of Freedom In Machine Learning and Statistics.Degrees of freedom is an important concept from statistics and Data Science(like Machine Learning). It is often employed to summarize the number of values used in the calculation of a statistic

Chapter 01: Introduction to Linear Regression

If you are someone who is familiar with Data Science, you must have realized that somewhere between Simple Linear Regression and Deep Neural Networks we grow up to become a Data Scientist.